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7 papers in the last four weeks, up 75% on the four weeks before. 0.1% of all new papers.

Jul 6Week of Sep 21

Latest papers 56

Oct 1, 2026cs.MA

After Cooperation Is Learned: Gradient Routing and Optimizer-Dependent Maintenance in Multi-Agent Reinforcement Learning

Cooperative MARL is commonly evaluated through cooperation discovery from random initialization, leaving open whether continued optimization can destabilize learned cooperation. Actor-critic comparisons can also conflate critic presence with value gradients entering shared actor representations. We study cooperation maintenance, defined as the survival of a behaviorally verified cooperative policy under continued training. We formulate maintenance as a right-censored event-time problem and compare matched warm starts: X0 allows value loss gradients to update shared actor features, X1 retains the critic while blocking those gradients, and X5 removes the learned critic as a critic-free reference. This isolates direct value-gradient access while controlling initialization, critic computation, and evaluation. Positive reward scaling preserves strategic preferences and equilibria while perturbing learning dynamics. Gradient audits confirm the intended routing pathways, and frozen-policy torso perturbations probe whether route-induced updates align with local cooperation boundaries. In confirmatory MinEx and CleanUp-lite experiments, higher scales selectively increase maintenance sensitivity in X0; X1 remains near the censoring ceiling, and X5 has no confirmed events in the tested settings. In CleanUp-lite, route-by-scale displacement is associated with reduced local cooperation margins; MinEx shows a weaker, optimizer-dependent effect. These results identify a conditional, scale-sensitive maintenance risk associated with direct value-gradient routing rather than a universal failure of critics.

Chaoyuan Hao, Wentao Yue, Tianyou Lai +4

Multi-Agent Reinforcement Learning Cooperation Critic Positive

Oct 1, 2026cs.AI

When Does Exercise-Specific Joint Selection Help? An Audit of Evaluation and Control Design

Exercise-specific joint selection can improve skeleton-based correctness classification, but what does that gain establish? We audit 1,057 repetitions from ten REHAB24-6 subjects, separating evaluation aggregation, subset structure, and temporal representation. The manual-subset kNN gain changes from 0.055 for pooled out-of-fold AUROC to 0.020 for equal-weight within-person AUROC; both paired intervals include zero. Among 1,000 dimension-matched random maps, 14 match or exceed the manual pooled result, versus 145 when bilateral structure and trunk inclusion are also matched. RBF-SVM retains a positive within-person gain, whereas logistic regression and a random-convolution comparator have negative point gains under that estimand. Sequence-order and paired-seed controls further qualify the interpretation. This exploratory audit shows why joint-selection claims require explicit estimands and structurally appropriate controls; it does not establish a new algorithm or clinical benefit.

Haotian Chen, Jingkun Yu, Yuning Zhang +1

School of Cyber Science and Technology, University of Science and Technology of China · SWJTU-Leeds Joint School, Southwest Jiaotong University · School of Education, Shanghai Jiao Tong University

Uncertain Joint Stiffness Physical Activities Joint Search Skeleton Positive

Sep 30, 2026cs.AI

Positive Ratings, Hidden Concerns: Employee Voice Disclosure in AI-Mediated Organizational Listening

Organizations started listening to employees through conversational AI agents alongside structured surveys. Little is known about what these channels change in what employees say when disclosure carries hierarchical risk. We report a field study inside a global management consulting firm whose process pairs a pre-survey with an adaptive AI voice interview on the same themes within one session. Across 44 first-session interviews (132 matched theme observations), 20-41% of sessions showed a favorable rating co-occurring with a substantive concern voiced later, depending on the favorability threshold. The Gioia analysis drew on 158 protective quotes from 65 eligible sessions. Disclosure rarely arrived unguarded: employees softened concerns, deflected accountability, and bounded how far they went, and this protective work tracked the perceived legitimacy of the listening structure. We develop a grounded model of bounded disclosure and derive four propositions for voice, channel and listening research. Silence, we argue, can persist inside expression.

Thilo Tamme, Michael Saatkamp, Alma Bonte +3

Technical University of Munich · LMU Munich · Massachusetts Institute of Technology

Disclosures Hear Interviews Survey Positive

Sep 28, 2026cs.AI

Evo2Team: When Do Evolved Skills Transfer? From Selection to Deployment

A skill bank that helps one multi-agent system may leave another's behavior unchanged. A transferred rule helps only when target agents act on it successfully. We study this path for routing and communication skills in Count-Frequency and AgentsNet, using teams of 4--32 agents and GPT and Qwen model ladders. Source evolution meets a joint quality, cost, model-tier, and confirmation goal in 14 of 16 settings. We then evaluate Evo2Team, which selects, adapts, and confirms source skills for the target team, alongside six frozen selectors across 28 transfer directions. Evo2Team's target-side exploration cost is below that of evolving a new target bank in every direction, even when reused reference evaluations are charged once. Twenty of 28 held-out outcomes meet the positive-transfer criterion, including three saved diagnostic tests. Selection alone does not explain these outcomes: KNN and CORAL choose different banks in two AgentsNet directions but produce identical recorded executions. When Evo2Team changes execution, gains can reach many tasks, as in a Count-Frequency direction that improves 28 of 32 tasks over KNN. Seven positive AgentsNet outcomes save 6.1--14.6% in deployment cost while using transferred skills on only three to six of fifteen tasks. In five earlier accepted directions, all 22 task records using transferred skills pass three fixed-graph confirmations, but four fail in recorded executions on new graphs. Graphs and model responses change together in this comparison. These results show that skill transfer must be assessed through the actions agents take, the tasks those actions reach, and the quality and cost of the final deployment.

Renxiang Wang, Jiaming Cui

Virginia Tech, Blacksburg, VA

Skills Cross-Task Knowledge Transfer Evolutionary Optimization Methods Positive

Sep 27, 2026cs.AI

Curating Merchant-Matching Training Data with Two Confidence-Gated Local LLM Judges

Merchant matching resolves a noisy payment descriptor to a retrieved merchant entity or returns no match. A key challenge in curating training labels is distinguishing teacher abstention from evidence that no acceptable entity exists: false no-match labels contaminate pseudo-labeled data, while conservative labeling reduces coverage. We investigate whether agreement between two local large language model judges improves pseudo-label reliability. A label is retained only when the judges agree, with separate ordered thresholds for selections and abstentions that guarantee disjoint positive and negative label sets. Retrospective replay on 2,000 expert-annotated queries shows that higher selection thresholds can improve positive-label purity, whereas higher abstention thresholds increase false no-match labels. At thresholds (0.86, 0.80), Muse Glimmer 30B and Gemma 4 31B jointly label 1,633 queries (81.7% coverage) at 96.88% purity; positive and negative purities are 99.47% and 93.38%. This exceeds either constituent model at the same thresholds by more than two percentage points, with lower coverage. A split-half check finds only 0.14 percentage points of threshold-selection optimism. A symmetric threshold of 0.86 adds 40 erroneous no-match labels, while 46 false abstentions persist even with no confidence threshold. Across five matched within-model comparisons, higher reasoning effort yields no clear F0.5 gain and increases median latency by 1.8-5.0 times. These results motivate separate thresholding and auditing for positive and negative pseudo-labels. The study establishes label purity, not student utility; fresh-data curation and student fine-tuning remain necessary to demonstrate downstream value.

Donghao Huang, Jinling Pei, Zhaoxia Wang

Research and Development, Mastercard, Arlington, VA, USA · School of Computing and Information Systems, Singapore Management University, Singapore

Judges Training Data Data-Curation Positive Adaptive Thresholding

Sep 20, 2026cs.CV

Confidence-Aware Teacher-Student Distillation for 3D Medical Segmentation

Medical image segmentation models typically rely on large amounts of densely annotated volumetric data, limiting their scalability across tasks and imaging modalities. This work addresses the challenge of predicting entire 3D anatomical structures from extreme annotation sparsity. An annotation-efficient student-teacher framework is proposed for automatic 3D medical segmentation that requires only a set of point prompts on a single 2D slice per volume, as input. A foundation model serves as an offline teacher, utilizing the provided point prompts from the selected slice to full-volume pseudo-annotations alongside their corresponding spatial confidence scores prior to student training. To mitigate the error propagation of noisy pseudo-annotations, a task-specific 3D student network is trained using a confidence-aware optimization strategy. By leveraging the teacher's pre-computed confidence scores, this strategy explicitly excludes statically uncertain regions of the pseudo-annotations from the loss calculation, while simultaneously emphasizing regions with higher confidence. Evaluated on 3D cardiac MRI datasets, our framework outperforms state-of-the-art semi-supervised methods, improving segmentation performance by up to 43.6%. Furthermore, it drastically reduces the manual annotation burden to just a few positive point prompts per volume, while improving surface boundary precision by up to 14.7% over the teacher and successfully recovering up to 34.1% of the performance gap toward the fully supervised upper bound.

Georgios Triantafyllou, Dimitris K. Iakovidis

Department of Computer Science and Biomedical Informatics, University of Thessaly, 2-4 Papasiopoulou st. 35131, Lamia, Greece

3D Medical Image Segmentation Teacher-Student Distillation Cardiac Magnetic Resonance Imaging Sparsity Positive

Sep 9, 2026cs.LG

A positive resolution of the gap-entropy conjecture

We prove the gap-entropy conjecture for fixed-confidence best-arm identification with independent unit-variance Gaussian arms, means in

[0,1]

, and a unique optimal arm. For each suboptimal arm

i

, let

Δ_i=μ_*-μ_i

be its gap from the optimal mean, and write

H=\sum_{i\ne *}Δ_i^{-2}

. Let

p_r

be the fraction of

H

contributed by arms with

2^{-(r+1)}<Δ_i\le2^{-r}

, and let

\mathrm{Ent}(I)=\sum_{r:p_r>0} p_r\log(1/p_r)

. Among all algorithms that identify the optimal arm with probability at least

1-δ

on every Gaussian instance, the optimal expected number of samples on a given instance, averaged over all permutations of the arm labels, is within absolute constant factors of

H(\log(1/δ)+\mathrm{Ent}(I))

. Moreover, there is an algorithm, independent of the instance, whose expected number of samples is bounded by a constant multiple of this quantity plus

g^{-2}\log\log(e^e/g)

, where

g=\min_{i\ne *}Δ_i

is the gap to the closest competitor.

P. M. Aronow, Nathan Kallus, Patrick Lopatto

Gaussian Primitives Positive

Sep 7, 2026cs.AI

Whose record is this? Diagnosing and authorizing record use in personalized multimodal models

Contextualized visual personalization can retrieve a true record yet apply it to the wrong visual subject. We formalize when a record may condition an answer as \emph{record authorization}: subject presence (

P

), record-edge validity (

E

), and answer support (

S

) must all hold. We call violations visual memory misbinding (VMM). We construct RecordAuth-Diag, a 3,690-case matched diagnostic suite that changes one image--record edge while holding the query, question, record text, and image multiset fixed. Card removal and nonce relabeling attribute these failures to supplied records. Raw-bank failures span Qwen-, Phi-, and Gemma-family interfaces: Gemma-3-4B-IT reaches 63.69% local unauthorized use at 25.75% clean recall. CoViP remains at 26.02%, versus 22.49% for its Qwen backbone at similar clean recall. Typed pre-generation authorization reduces Qwen card exposure on RecordAuth-Diag from 43.63% to 3.06%, while positive recall changes from 86.26% to 60.90%. Full

P\wedge E\wedge S

validation uses 560 localized DAVIS cases: top-1 relevance and typed authorization have comparable release (28.93% and 28.39%) but 6.79% and 0.89% unsafe release, respectively. Of the 33 additional unsafe cases removed, 27 are support, 4 edge, 2 clean, and 0 boundary cases. Thus the observed increment is an

E\wedge S

decision dominated by support, not an edge check alone. Appearance supplies

E

evidence only conditional on

P

; authenticated subject tokens instantiate the missing presence witness as a sufficiency control. The claims concern the evaluated contracts, not natural prevalence, consent, or visual identity

Xinyu Mao, Junsi Li, Chenyang Liu +2

University of Electronic Science and Technology of China

Reference-Guided Multimodal In-Context Verification Witnesses Multimodal Model Personalization Positive

Aug 31, 2026cs.LG

Does On-Policy Distillation Really Distill? From Noisy Teacher to Self-Improvement

On-policy distillation (OPD) offers dense token-level supervision as an alternative to the sparse outcome-level advantages of reinforcement learning with verifiable rewards (RLVR). However, the teacher scores student-generated trajectories that are inherently off-policy for it, so the reliability of its supervision, and hence the source of the student's improvement, remains unclear. We quantitatively analyze teacher supervision during OPD training and find substantial noise whose prevalence increases with teacher scale. Surprisingly, the student policy is insensitive to such noise, converging to comparable performance regardless of whether noisy supervision is retained or removed. Does OPD distill at all? By analyzing what drives its gains, we find that learning concentrates on low log-probability tokens, and using a single fixed negative advantage matches the performance of teacher-provided ones. This suggests that OPD works largely by suppressing low log-probability tokens, which requires no teacher. These findings motivate On-Policy Self-Adaptation (OPSA), a supervision-free method using entropy-adaptive negative advantages. It assigns stronger learning signals to high-entropy positions, suppressing tail tokens, and evenly redistributing probability mass among head tokens. Compared with the base \texttt{Qwen3-1.7B}, OPSA improves Avg@32 by 35.41 points on AIME24, corresponding to a 263% relative gain, and more than doubles Pass@32 across all three benchmarks. It also outperforms OPD by 16.77 points in Avg@32 on AIME24. Extensive experiments and analyses across model families and tasks further demonstrate its effectiveness and generalizability.

Yi Ding, Ruqi Zhang

Department of Computer Science, Purdue University, USA

Online-Policy Distillation Teacher Recursive Self-Improvement Positive

Aug 31, 2026math.CO

Exponential random graph models with soft clique constraints

Let

r\geq3

be fixed, and let

\mathbf{G}_n

be the set of all simple graphs with vertex set

[n]=\{1,\ldots,n\}

. We consider an exponential random graph model which gives higher probability to

G \in \mathbf{G}_n

than to

H \in \mathbf{G}_n

if

G

has fewer

r

-cliques than

H

. But all graphs in

\mathbf{G}_n

have positive probability. The degree to which graphs with fewer

r

-cliques are given higher probability is determined by a positive weight

w

. We prove that, asymptotically almost surely as

n \to \infty

, a random graph from

\mathbf{G}_n

has a vertex partition into

r-1

parts of roughly equal size, the density of edges between the parts is close to

1/2

, and for every

\varepsilon > 0

the density of edges within any part is less than

\varepsilon

. The asymptotic structural properties are independent of the weight

w

as long as it is positive. We also extend the result to the context of several clique sizes, each one with its own weight.

Yasmin Tousinejad, Vera Koponen

Inhomogeneous Random Graphs Maximum Independent Set Finite Time Analysis Positive

Aug 31, 2026cs.IR

Learning from What You Retrieve: Online RL Fine-Tuning for Semantic Retrieval

In large-scale e-commerce retrieval, dual-encoder retrievers are op- timized for contrastive similarity, whereas downstream rerankers capture finer-grained relevance preferences; this objective mis- match limits end-to-end retrieval quality. Reinforcement Learning offers a way to use reward-model feedback for retriever adaptation, but we observe that standard policy-gradient updates can degrade embedding geometry, especially when the document index must remain frozen due to industrial constraints. To address this, we propose PAO (Positive-Advantage-Only), a selective RL optimization method. Our analysis reveals that in- discriminate penalization of negative samples (pushing away) in a frozen high-dimensional space disrupts pre-trained semantic man- ifolds. PAO selectively applies gradient updates only to retrieved items with positive advantages, effectively pulling query embed- dings toward high-reward regions while preserving global topo- logical stability. Experiments on both a massive industrial dataset and public benchmarks demonstrate that PAO significantly outper- forms standard RL and distillation baselines.

Shaowei Wei, Chong Huang, Songtao Fang +3

Alibaba Group Hangzhou, China

Reinforcement Fine-Tuning Model Fine-Tuning Positive

Aug 30, 2026cs.CL

Detecting Hidden Chain-of-Thought in Large Language Models with Linguistic, Behavioral, and Mechanistic Indicators

Large language models often answer complex reasoning questions without revealing intermediate steps, raising whether they reason latently or complete patterns. We propose the Hidden CoT Detection Score (HCDS), a comparative behavioral and mechanistic signal measuring whether neutral-prompt behavior aligns more closely with explicit CoT or explicit no- CoT. Here, hidden CoT operationally denotes this neutral-prompt CoT-like alignment; HCDS does not directly observe or prove an unexposed reasoning trace. On GSM8K, HCDS is significantly positive for both Qwen3-4B variants (Thinking

+1.87

,

p = 1.2 \times 10^{-7}

; Instruct

+1.41

,

p = 1.9 \times 10^{-4}

), replicates across a different inference stack and quantization within

0.08

(

+1.80

and

+1.45

), and is not significantly positive in seven of eight length-adjusted calibration-control cells. The unadjusted score produces large positive scores on single-step arithmetic and numeric factual lookup. The variants also respond differently to no-CoT instructions: Instruct complies from the prompt alone, whereas Thinking continues reasoning and requires intervention. These findings show stronger, less prompt-conditional CoT-like behavior in the reasoning-tuned model, consistent with but not proof of latent reasoning. HCDS thus investigates latent reasoning without relying on models' self-reported traces.

Armaan Singh, Ryan Trinh Le, Jasmine Kaur +5

1Stanford University · 2York University · 3Facebook

LLM Reasoning Strategies Reasoning Traces Chain-of-Thought Reasoning Linguistics Positive

Aug 13, 2026cond-mat.stat-mech

Thermodynamics of Learning: A Typed Four-Component Accounting of Memory, Fit, and Value

What a finite learning device has recorded and what will hold value for it on future tasks are not the same quantity. We develop a typed accounting for finite-state learning devices that separates four components: a training-side fit functional

Φ_{\mathrm{fit}}

, the record-correlation stock

J_{D}=I(M;D)

, an update-side search ledger

σ_{M}

, and an operational capital value

V(M;T,b)

. This value is the work gap between an informed protocol class and a blind class obtained by deleting the memory-read port and re-optimizing from scratch. (I) Separation: for every

n

, there is a device family on which record correlation and world correlation grow by

n\ln 2

while the capital gain is exactly zero. In the

\mathrm{flat}^{*}

regime, data-free updates never increase

V

. (II) Capitalization ledger: an exact

\mathrm{flat}^{*}

extraction identity and a universal ledger identity give, for (F5

'

)-stable

M

-local updates under a no-discarded-record-correlation condition (f), the bound

η_{\mathrm{cap}}\le 1

for the capitalization efficiency

η_{\mathrm{cap}}=ΔV/(k T\,σ_{M})

, together with necessary and sufficient conditions for equality. (III) Value retention: for the retention gap

L_{\mathrm{gen}}

and retention ratio

ρ_{\mathrm{gen}}

(the former carries no sign constraint; the latter is defined for positive training-side value and is not confined to

[0,1]

) we give a two-layer alignment domain: an exact exchange rate between value and the side-information-adjusted record fit

I(M';D\mid Y)

without any record-side-information independence assumption, and a raw record-stock exchange rate under a joint side-information neutrality condition

(M,D)\perp Y

, whose boundary is marked by an explicit one-time-pad witness. These are statements about finite-device value retention under task-distribution shift, not a theory of statistical generalization.

Akihito Sudo

ZeroStruct Inc.

Value Ledger Thermodynamics Gradient Staleness Positive Conditional Independence

Aug 9, 2026cs.GT

Voting Method Synthesis on an Infinite Domain: A Possibility Theorem for Positive Involvement

A common problem in social choice is to determine whether there is a social choice procedure, such as a voting method, satisfying some desired criteria. Computer-aided methods such as SAT solving can sometimes answer these questions. However, under typical encodings, a SAT solver may only synthesize a voting method on a finite domain, while we may want one on an infinite domain, such as the domain of all preference profiles for a fixed number of candidates but any finite number of voters. In this paper, we use an approach based on reasoning with constrained Horn clauses and computation with polyhedra to synthesize a voting method on an infinite domain. We then use SMT and Lean to verify its properties. Our main result is a possibility theorem about four well-known criteria from voting theory: the Condorcet winner and loser criteria, positive involvement, and resolvability. Previous work has shown that for five or more candidates, there is no voting method satisfying these axioms, and that for four candidates, there is no method satisfying these core axioms plus one more invariance axiom. Here we show that for four candidates, there does exist a method satisfying the core axioms and more.

Wesley H. Holliday

University of California, Berkeley

Satisfiability Majority Voting Axiom Positive Infinities

Aug 3, 2026cs.LG

Inverted Detection and Control in Steering Vectors

Steering vectors (SVs) are widely used to influence the expression of concepts (e.g., truthfulness) in large language model outputs. A key assumption underpinning SVs is that they are linearly discriminative with respect to the concept: representations of texts that exhibit the concept are more aligned with the SV than those that do not, motivating shifts along the positive or negative SV direction to respectively promote or suppress the concept. In this work, we identify an inverted detection-control phenomenon in which some highly discriminative SVs that are aligned with positive representations can consistently promote the opposite behavior. We refer to such vectors as inverted-steering vectors (ISVs). We provide a geometric characterization of ISVs' effects, finding that steering along these directions systematically pushes representations in discriminative downstream heads as if the concept were absent, even prior to decoding. Motivated by this analysis, we propose an approach for distinguishing ISVs without requiring generation or associated response scoring. This enables targeted sign flips, which we use to improve a foundational detection-based steering pipeline via Inference Time Intervention (ITI). Our approach improves results in 27/30 experiments, ranging from +0.9% to +138%. We evaluate our findings on Gemma 3 12B, Qwen 2.5 14B, and Olmo 3 7B across 5 concepts.

Max Torop, Aria Masoomi, Jennifer Dy

Northeastern University

Vectors Instruction-Tuned Models Positive

Jul 27, 2026cs.CV

Rethinking Classifier-Free Guidance in On-Policy Diffusion Distillation

On-policy distillation (OPD) adapts diffusion models by querying a teacher along trajectories generated by the current student, but how it should behave under classifier-free guidance (CFG), a default component of modern diffusion systems, remains poorly understood. Existing OPD methods naturally extend velocity matching to the CFG-composed prediction, directly matching teacher and student guided velocities. We show that this objective is under-identified at the branch level: positive- and negative-branch errors can compensate in the guided prediction. Through two contrasting cases, we find that naive matching remains effective under shared negative conditioning, where both branch errors decrease jointly. When the model's native CFG schema retains privileged information in the teacher's negative branch that is unavailable to the student, however, this joint reduction breaks down and the composed objective induces antagonistic branch-error dynamics, reducing the positive-branch error while increasing the negative-branch error. We term this failure mode Negative Branch Asymmetry (NBA). To address NBA, we introduce Positive--Direction Matching (PDM), a branch-aware OPD objective that separately constrains the positive prediction and the CFG conditional direction. We apply PDM to dense-to-sparse video control, where naive guided matching is highly sensitive to inference guidance scales, while branch-aware supervision enables more robust and effective knowledge transfer.

Bingnan Li, Haozhe Wang, Haozhong Xiong +5

Qwen Business Unit of Alibaba

Classifier-Free Guidance Positive

Jul 25, 2026cs.GT

Fair Division with Strictly Increasing Valuations: A Tight Threshold for Two-Agent EF1 and PO

We study whether strictly positive marginal values restore the compatibility of envy-freeness up to one good (EF1) and Pareto optimality (PO) for indivisible goods. For two agents, we identify the exact threshold in the number of goods. Every instance with at most seven goods and strictly increasing valuations admits an allocation that is both EF1 and PO, without any submodularity assumption. In contrast, we construct an eight-good instance with normalized, integer-valued, strictly increasing, submodular valuations in which every EF1 allocation is strictly Pareto dominated. Thus, eight goods are necessary and sufficient for a two-agent counterexample. Finally, we strengthen the three-agent NP-hardness result of Chandramouleeswaran and Nimbhorkar (2026): deciding whether an EF1 and PO allocation exists remains NP-hard for normalized, integer-valued, monotone submodular valuations even when zero marginals are confined to eight fixed agent-good pairs, all involving a single agent.

Nicholas Teh

University of Oxford, UK

Welfare Submodular Functions Pareto Frontier Algorithmic Fairness Value Decision Thresholds+1

Jul 23, 2026cs.AI

Logic Programming Semantics for Causal Processes

Motivated by challenging modelling issues in the life sciences, we investigate the relationship between logic programming semantics and the eventual states of causal processes compatible with those logic programs. More precisely, we show that while stable models of positive logic programs correspond to the eventual states of processes commencing from a neutral state and continuing undisturbed indefinitely, supported models describe the eventual states reachable from arbitrary starting points. This also contributes to the discussion of the appropriate semantics for logic programming as a causal rule language, adding a temporal perspective to recent interpretations of the stable and supported model semantics from an explanatory viewpoint of causality.

Felix Weitkämper

German University Of Digital Science Potsdam, Germany

Logic Causal State-Tracking Positive

Jul 23, 2026cs.LO

Anti-Goal Reasoning: Rethinking the Theory of Goal Reasoning in Non-Axiomatic Logic

Goal reasoning in Non-Axiomatic Logic (NAL) explains how an adaptive system derives means for realizing desired events under insufficient knowledge and resources. However, the representation of avoidance is less clear. A common convention is to express avoid $G$'' as the goal sentence

\neg G!

'', but this notation conflates two different readings: pursuing the negated event

\neg G

, and avoiding the positive event

G

. This paper shows that the conflation can produce a paradoxical case in which an avoidance intention is converted into a positive goal to act merely because acting is usually followed by the absence of hurt. Starting from NAL's basic definition of goals, the framework is extended with a corresponding definition of anti-goals, so that avoidance can be represented without treating it as the pursuit of a negated event. Finally, a mental operation,

\op{prevent}

, is introduced to connect anti-goal reasoning with ordinary goal reasoning in cases of active prevention. Four minimal case studies check that the resulting rules distinguish pursuit, passive avoidance, active prevention, and withholding action to preserve a desired event.

Bowen Xu

Department of Computer and Information Sciences, Temple University, USA

Axiom Positive Theory

Jul 15, 2026cs.LG

PUe: Biased Positive-Unlabeled Learning Enhancement by Causal Inference

Positive-Unlabeled (PU) learning aims to achieve high-accuracy binary classification with limited labeled positive examples and numerous unlabeled ones. Existing cost-sensitive-based methods often rely on strong assumptions that examples with an observed positive label were selected entirely at random. In fact, the uneven distribution of labels is prevalent in real-world PU problems, indicating that most actual positive and unlabeled data are subject to selection bias. Building on the SAR-PU propensity-weighted framework of Bekker et al., we study a PU learning enhancement (PUe) framework using normalized propensity scores and normalized inverse probability weighting (NIPW). PUe's main contributions are a normalized inverse-probability-weighted PU risk formulation; additional theoretical analyses of normalized sample-weight error and common PU estimators under biased labeling; regularized deep propensity-score estimation; integration with modern cost-sensitive PU methods; and support for selectively labeled negative classes. Experiments on MNIST, CIFAR-10, and ADNI demonstrate improvements over several PU baselines under non-uniform label distributions.

Xutao Wang, Hanting Chen, Tianyu Guo +1

Huawei Noah’s Ark Lab

Positive--Unlabeled Learning Importance Weighting Selection Bias Biases Positive Causal Inferences

Jul 15, 2026cs.CV

Marker-free deformable registration and fusion for augmented reality-guided positive margin localization during tumor resection surgery

Positive margins in head and neck oncologic surgery require mapping specimen-side pathology findings to the patient resection bed. This is challenging because pathologists identify the positive margin on slices of the resected, deformed specimen, while surgeons must relocate the corresponding site on the resection bed using only verbal descriptions and no visual guidance. We present a marker-free augmented reality (AR) workflow for mapping a margin label from a three-dimensional specimen scan to the resection bed. The method combines contour-constrained deformation, residual alignment to a depth scan, surface-based fusion to a head-mounted display, and target projection onto the reconstructed bed. Bead-suture correspondences estimate specimen deformation, whereas patient-to-display fusion does not require external fiducial markers. Following formative experiments, five residents and surgeons performed cadaveric cheek and scalp re-resection tasks under verbal guidance, verbal guidance with specimen examination, and AR guidance. Deformation target errors were

7.63 \pm 3.74

mm for the cheek and

3.72 \pm 1.02

mm for the scalp; residual specimen-to-bed distances were

2.43 \pm 2.15

mm and

2.19 \pm 1.06

mm, respectively. Fusion error did not differ significantly between marker-free and marker-based methods on either cadaver; overall marker-free fusion error was

2.15 \pm 0.87

mm. End-to-end margin localization error decreased from

21.40 \pm 3.84

mm with verbal guidance and

16.09 \pm 4.30

mm with specimen examination to

6.19 \pm 1.79

mm with AR guidance (

p < 0.001

). Online fusion required

5.23 \pm 0.34

s. These results demonstrate effective marker-free AR guidance for positive-margin localization and support more precise tumor resection.

Yue Yang, Annie Benson, Matthieu Chabanas +7

Vanderbilt University, 1400 18th Avenue South, Nashville, Tennessee, 37212, USA · Vanderbilt University Medical Center, 1211 Medical Center Drive, Nashville, Tennessee, 37232, USA

Image Registration Augmented Reality Deformable Cnn Object Localization Digital Pathology Positive

Jul 13, 2026cs.AI

Playful AI in Professional Email: A Field Experiment on Tone and Recipient Engagement

Large language models (LLMs) are rapidly reshaping workplace communication, yet whether AI-assisted writing changes how recipients actually behave, and through what channel, remains unknown. Here, in a randomized crossover field experiment, 121 employees across six companies sent work emails under three conditions over three weeks: unaided writing, GPT-5 rewriting in a playful tone, and GPT-5 rewriting in a professional tone. Across 16,880 emails, playful editing increased emotional positivity (B=+0.068, p<0.001), and professional editing decreased it (B=-0.041, p<0.001), yet neither condition directly altered open rates, reply rates, or response times. Instead, within-sender positivity strongly predicted both opening (OR=2.05) and replying (OR=3.32, p<0.001), a significant indirect pathway through which AI editing shaped behavior, in the absence of any direct effect. These findings suggest that AI-assisted communication shapes workplace engagement not through its use, but through the emotional tone of the language it produces.

Ziv Ben-Zion, Teddy Lazebnik

School of Public Health, Faculty of Social Welfare and Health Sciences, University of Haifa, Haifa, Israel · Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA · Department of Information Systems, University of Haifa, Haifa, Israel +1

Ai-Assisted Writing Analytics Artificial Intelligence Systems Positive Edit

Jul 11, 2026cs.LG

The Differential Neural Tangent Kernel and Its Positivity

The Neural Tangent Kernel (NTK) is one powerful tool for analyzing the training dynamics of neural networks in the over-parameterized regime. Recently, the theoretical framework has been extended to physics-informed neural networks (PINNs) for solving linear PDEs, one highly popular class of neural PDE solvers. In the analysis, the positivity of the associated NTK plays a fundamental role. However, establishing the positivity of the NTK for PINNs is highly challenging, due to the presence of multiple differential operators. In this work, we propose a new theoretical framework, called Differential Neural Tangent Kernel (DNTK), for analyzing PINNs through the lens of the NTK, and establish the positivity of the infinite width DNTK for both shallow and deep neural networks for a wide class of activation functions, including RePU and smooth but non-polynomial activations, for all linear differential operators. These theoretical results lay the foundation for the analysis of gradient type algorithms for training PINNs.

Bangti Jin, Longjun Wu

Department of Mathematics, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, P.R. China

Neural Tangent Kernel Parametric Physics-Informed Neural Network Partial Differential Equations Activation Functions Positive

Jun 26, 2026stat.ML

Surprises in Proper Positive-Only Learning

Binary classification from positive-only samples is a variant of PAC learning in which the learner receives i.i.d. samples from the positive region of an unknown target concept, but is evaluated under the original distribution (which places mass on both positive and negative regions). This model dates back to Natarajan [1987, STOC], and the characterization of improper learning is well-known -- it even appears in textbooks. The characterization of proper positive-only learning, however, has long remained open. In this work, we revisit and settle this question: a concept class is properly learnable from positive-only samples if and only if it has finite VC dimension and satisfies a new combinatorial condition, which we call uniform exterior separability. Together with several separation results, this characterization reveals a surprisingly rich landscape that differs sharply from standard PAC learning: proper and improper learning are separated, randomized and deterministic proper learning are separated, there are classes for which no ERM is a learner, and finite VC dimension does not suffice even for non-uniform learning. Along the way, we introduce new combinatorial dimensions that we believe can be of broader interest in learning theory.

Shai Ben-David, Farnam Mansouri, Anay Mehrotra +1

University of Waterloo and Vector Institute · Stanford University · Yale University

Learnability Positive--Unlabeled Learning Positive Surprisal Voice Conversion

Jun 25, 2026cs.CL

DiARC: Distinguishing Positive and Negative Samples Helps Improving ARC-like Reasoning Ability of Large Language Models

The Abstraction and Reasoning Corpus (ARC) contains tasks that require summarizing patterns from limited grid samples and predicting output grids. Recently, many large language model based approaches have attempted to transform it into a text-based reasoning task. However, methods based on open-source models have generally yielded unsatisfactory results, while those relying on closed-source models are too costly. Current efforts mainly focus on data augmentation, constructing ARC-like data for more comprehensive supervised fine-tuning. In this work, we argue that solving ARC-like problems requires not only positive sample supervision but also the ability to improve model reasoning by distinguishing negative samples. To this end, we draw on the idea of preference alignment and propose DiARC, a method that constructs preference pairs to enable the model to distinguish between them. Specifically, we propose three ways to construct negative samples, including output-level visual transformations, DSL-level rule inversion, and task-specific rule editing. The resulting negative samples provide informative near-miss alternatives while keeping the observed demonstrations unchanged. Experimental results across multiple ARC-like benchmarks show that DiARC consistently improves performance over baseline models. The code is released at https://github.com/szu-tera/DiARC.

Yuxuan Yang, Feiyang Li, Yile Wang

College of Computer Science and Software Engineering, Shenzhen University

Reasoning Skills Negative Results Text Corpora Positive

Jun 17, 2026cs.CL

Improving Medical Communication using Rubric-Guided Counterfactual Recommendations

Text-based telemedicine increasingly relies on lightweight patient feedback, however, such feedback primarily reflects perceived communication quality rather than medical accuracy. We introduce an LM-guided counterfactual recommendation pipeline that discovers and refines interpretable communication features such as tone, personalization, actionability and completeness in addressing patient concerns, without interfering with the medical content. These features are used together with patient-doctor interaction metadata to estimate positive feedback. At inference time, the system searches over low-cost ordinal feature changes and recommends minimal communication changes predicted to increase the probability of positive feedback, while independent auditor models test whether these gains generalize beyond the selection model. Across interactions, recommendations yield a mean +6.41% gain in predicted positive feedback probability under independent auditors, and are non-negative for 93.31% of recommendations. These results suggest that small, interpretable communication changes can capture most predicted gains while preserving the doctor's control over medical reasoning and final wording.

Adrian Cosma, Nicoleta-Nina Basoc, Andrei Niculae +2

IDSIA, Dalle Molle Institute for Artificial Intelligence · National University of Science and Technology POLITEHNICA Bucharest

Feedback Positive Recommendation

Jun 15, 2026cs.AI

Symbolic Informalization: Fluent, Productive, Multilingual

Symbolic informalization enables a reliable conversion of formal mathematics to natural language. It has the potential to make machine-checked content human-readable without loss of precision. In a traditional proof system usage, symbolic informalization generalizes the limited mechanisms of syntactic sugar into the ordinary language of mathematics. In a setting where proofs are constructed by artificial intelligence and autoformalization, symbolic informalization can explain what precisely has been constructed. This paper outlines the project Informath, which aims to show how symbolic informalization can produce fluent text with a reasonable development effort and address multiple formal and natural languages. Informath is based on an interlingual architecture, where Dedukti works as a hub between different proof systems (Agda, Lean, Rocq) and Grammatical Framework (GF) takes care of linguistic correctness and variation in different natural languages.

Aarne Ranta

Department of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg

Formalization Symbol-Natural Language Multilingual Positive

Jun 9, 2026cs.LG

Revisiting Positive Samples in Graph Contrastive Learning: From the Perspective of Message Passing

Graph Contrastive Learning (GCL), which trains graph encoders by maximizing similarity between positive samples and minimizing it between negative ones, has emerged as a mainstream graph pre-training paradigm. It is widely recognized that positive samples are essential in GCLs. Ideally, maximizing the similarity of positive samples enables graph encoders to capture intrinsic semantic and patterns of graph data. However, we discover an interesting phenomenon: GCLs can achieve competitive performance even without positive samples. This motivates us to revisit the fundamental mechanism of positive samples in GCLs. From the perspective of Dirichlet energy, we theoretically finds that message passing, a key mechanism in graph encoders, trivializes the maximization of positive samples, preventing GCLs from effectively learning from positive samples. To address this, we propose SPGCL to mitigate the trivialization caused by message passing and restore the learning efficacy of positive samples. Specifically, we find that high Dirichlet energy features help positive samples provide effective learning signals while low Dirichlet energy features contribute little to positive learning signal but is useful for positive sampling. Based on this, SPGCL propagates only high Dirichlet energy features and uses low energy features to construct a probability matrix for reliable positive sampling. Extensive experiments demonstrate the effectiveness of SPGCL.

Lianze Shan, Ningchong Wang, Jitao Zhao +2

School of Computer Science and Technology, Tianjin University, Tianjin, China · School of Future Technology, Tianjin University, Tianjin, China.

Graph Representation Learning Contrastive Learning Positive Propagation Perspective

Jun 4, 2026cs.SC

A Finite Certificate for the Positive $n=9$ Vasc Inequality

We prove the positive-real

n=9

case of the Vasc cyclic inequality. The proof was obtained with human-guided assistance from the AI agent MechMath Agent Team: the human-readable part reduces the rational inequality to a homogeneous polynomial inequality, fixes a cyclic maximum, and parametrizes each sorted fixed-maximum cone by cumulative gaps; the finite part is a certificate covering all

8!=40320

sorted cones. MechMath Agent Team generated the certificate verification workflow through Python tool calls, including the case split, verification programs, and terminal classifications. The published certificate has

36815

coefficient leaves,

2236

ordinary Polya multiplier leaves, and

1269

AM-GM midpoint overlay leaves. Human authors audited the mathematical reductions and verification logic, and a separate artifact contains the certificate, an independent verifier, and a from-source rebuild route.

Dakai Guo, Ruichen Qiu, Yichuan Cao +1

State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, and the School of Mathematics, University of Chinese Academy of Sciences, Beijing, People’s Republic of China.

Finite-Sample Certificates Certificates Upper Bounds Positive

May 29, 2026cs.CV

Omni-Supervised Motion Editing: Balancing Change and Invariance through Positive-Negative Learning

Text-based human motion editing aims to modify existing motion sequences according to natural language instructions while maintaining the consistency of the original motion. Existing diffusion-based approaches often rely on heuristic similarity cues or coarse global conditioning, leading to motion distortion and suboptimal semantic alignment. The key challenge lies in balancing change (i.e. precisely editing target regions) and invariance (i.e. preserving unedited parts). To handle such challenge, we propose an Omni-Supervised Positive-Negative Learning framework, named OmniME. Our method integrates three complementary components: (1) retrospective feature supervision that enforces coarse-to-fine consistency across transformer layers,(2) motion preservation mechanism that focuses on subtle variations according to the source-target similarity, and (3) triplet-based semantic alignment that strengthens text-motion correspondence. Together, these components form a unified supervision paradigm that balances change and invariance. Extensive experiments on the MotionFix and STANCE Adjustment datasets demonstrate that OmniME achieves state-of-the-art performance in editing alignment, validating the effectiveness of our unified learning framework. Our source codes and models have been released at: https://github.com/rocket-ycyer/OmniME.git

Zhenwu Shi, Jingyu Gong, Peiwei Wang +7

Shanghai Institute of Artificial Intelligence for Education, East China Normal University, China · School of Computer Science and Technology, East China Normal University, China · Shanghai Key Laboratory of Computer Software Evaluating and Testing, China +4

Text-To-Motion Generation Image-Text Alignment Motion Positive

May 28, 2026cs.LG

HPO: Hysteretic Policy Optimization for Stable and Efficient Training under Sparse-Reward Regime

We investigate a narrow but common failure mode of GRPO-style reinforcement learning in the context of sparse verifiable rewards: early updates contain more responses with negative advantages than those with positive advantages, while response-level length normalization ties the magnitude of the update to the length of the output. We propose Hysteretic Policy Optimization (HPO), a minimal modification of GRPO that reduces the weight of negative-advantage updates and replaces per-response length normalization with mean-length normalization. We further introduce Adaptive HPO (A-HPO), which sets the hysteretic weight based on batch-level advantage-sign statistics, thereby removing the need for tuning a fixed hysteretic weight. In our TeleLogs and Countdown experiments, A-HPO improves the reward per update compared to GRPO, with the largest gains in early sparse reward regimes. On TeleLogs, A-HPO achieves a final reward of 0.84, outperforming SAPO by 5%, GSPO by 11%, and GRPO by 15%, while maintaining a comparable response-length. On Countdown, A-HPO achieves the largest gains in initial and most difficult configurations across 1.5B-7B models. Ablation studies on the hysteretic weight show that the gains of A-HPO come from better balancing the contributions of positive and negative advantages compared to positive-only or fully symmetric updates.

Mohamed Sana, Nicola Piovesan, Antonio De Domenico +2

Paris Research Center, Huawei Technologies, Boulogne-Billancourt, France · Huawei Technologies, China

Frictive Policy Optimization Group-Based Reinforcement Learning Verifiable Rewards Positive

May 26, 2026cs.CV

PlayClass: Automated Play Behaviour Classification in Poultry

Automated monitoring of animal welfare has largely targeted negative indicators, leaving positive welfare behaviours such as play underexplored. To address this gap, we present PlayClass, a pipeline for play-behaviour classification in poultry from top-down pen video. The pipeline leverages long-duration tracking with SAM 3 via YOLO-guided chunk boundaries to minimise identity errors in point-based prompting, and frozen embeddings from image and video foundation models for play action classification. Although handcrafted motion features from tracked masks alone achieved competitive accuracy, V-JEPA 2.1 consistently outperformed all other backbones across model scales, reaching 77.0 macro-averaged F

_1

when combined with handcrafted features. Despite this result, the dataset remains challenging due to play sub-types sharing similar kinematic profiles with non-play and inter-bird occlusion. Overall, our work provides encouraging evidence towards automated frameworks for play behaviour classification in poultry.

Prince Ravi Leow, Neil Scheidwasser, Rebecca Oscarsson +3

Section for Health Data Science & AI, University of Copenhagen · 1Section for Health Data Science & AI, University of Copenhagen · Department of Infectious Disease Epidemiology, Imperial College London +2

Species Bird Automated Motion Occlusion Positive

May 26, 2026cs.CV

Can Retrieval Heads See Images? Multimodal Retrieval Heads in Long-Context Vision-Language Models

Large vision-language models increasingly rely on long-context modeling to reason over documents, hour-level videos, and long-horizon agent trajectories, requiring them to locate relevant evidence across interleaved text and images. Prior work has studied this behavior using retrieval heads in large language models, but its copy-based criterion does not directly apply when evidence appears in images. We introduce a multimodal retrieval head detection method that scores attention from question tokens to textual or visual evidence. With this method, we show that multimodal retrieval heads are sparse, intrinsic, and causally important: only 4.4-10.2% of attention heads account for 50% of the positive retrieval-score mass, and masking the top-5% selected heads reduces performance on two tasks from MMLongBench by 42.5 and 62.3 percentage points, while random-head masking is far less damaging. Further analysis shows that these heads are partly shared across modalities yet remain dynamic within each modality, with image retrieval heads changing more than text retrieval heads as context length and haystack modality change. Without further training, we find that these heads can also be used directly to rank visually rich documents: on MMDocIR, Qwen3-VL-8B selected-head scoring improves Recall@1 by 7.7/7.4 macro/micro points for page retrieval and 6.3/6.8 points for layout retrieval over the strongest reported baseline.

Aaron Branson Cigres Li, Zhaowei Wang, Yu Zhao +9

1HKUST · University of Edinburgh · 4CUHK +2

Multimodal Retrieval Recent Vision-Language Models Visual Evidence Question-Answering Benchmarks Positive

May 26, 2026cs.AI

Boosting Knowledge Graph Foundation Models via Enhanced Negative Sampling

Knowledge graphs (KGs) have become the core backbone of numerous downstream tasks such as question answering and recommender systems. However, despite all this, KGs are often very incomplete. To perform zero-shot knowledge graph completion in unseen KGs, which have different relational vocabularies from those used for pre-training, KG foundation models (KGFMs) receive a wide range of attention. Existing KGFMs often perform training using random negative triples, which are constructed by replacing the head or tail entity of a positive triple with a random entity. However, these negative triples are often constructed with limited quality, providing weak supervision for KGFM training. In this paper, we propose a simple yet effective adaptive negative sampling approach, KMAS, to enhance existing KGFMs. KMAS constructs hard negative triples through the updated relation embeddings generated from the existing KGFM's relation encoder. To further adaptively align with the evolving capability of the KGFM during the training process, KMAS adjusts the ratio of hard negative triples dynamically throughout the whole training process: after a warmup phrase, it increases the ratio linearly and then decreases linearly. Extensive experiments are conducted over 44 data sets. Experimental results demonstrate that our proposed negative sampling method can enhance many SOTA KGFMs without requiring excessive additional time or memory consumption.

Yinan Liu, Wenjin Xu, Zhiyuan Zha +2

Northeastern University Shenyang, China

Knowledge Graph Completion Knowledge Graphs Wireless Foundation Models Negative Results Positive Sentence Encoders

May 26, 2026cs.LG

PATE-TabTransGAN: Differentially Private Synthetic Tabular Data Generation via Transformer-Based Student Discrimination

Generating high-fidelity synthetic tabular data under formal differential privacy guarantees remains an open challenge. Methods that provide strong theoretical protection typically sacrifice the modeling of inter-feature dependencies required for realistic synthesis, while architectures that excel at capturing complex column relationships offer only empirical privacy guarantees. We present PATE-TabTransGAN, a generative framework that integrates the Private Aggregation of Teacher Ensembles (PATE) mechanism with a Transformer-based student discriminator to jointly address both requirements, and employs a GNMax RDP accountant for numerically stable privacy accounting. An ensemble of Logistic Regression teachers trained on disjoint partitions supervise the student via noisy-aggregated labels, and a residual generator is optimized against this differentially private student, inheriting formal (ε, δ)-DP guarantees by post-processing. PATE-TabTransGAN was compared with PATE-GAN, DP-GAN, and DP-CTGAN, considered state-of-the-art in differentially private tabular synthesis. Experiments conducted on four tabular benchmarks (Adult, Breast, Cardio, Cervical) confirmed the high quality of the proposed method: PATE-TabTransGAN attains the best or tied-best AUROC on all four datasets. On AUCPR it matches the strongest baseline on Cardio, leads on Cervical, and trails on Breast; on Adult, we demonstrate that AUCPR is highly sensitive to positive-class convention, and that the observed gap is consistent with a convention difference between evaluation pipelines rather than a synthesis deficit.

M. Youssef, M. Woźniak

Wrocław University of Science and Technology, Wrocław, Poland

Synthetic Tabular Data Privacy-Utility Trade-Offs Conditional Gans Student Positive

May 24, 2026math.AG

Positivity in classical enumerative geometry: a case study in synchronized AI-assisted mathematics

We study the symmetric polynomial

\prod_{α\in A_{n,d}}\bigl(1+α_1 x_1+\cdots+α_n x_n\bigr)

where

A_{n,d}:=\{α\in\mathbb{Z}_{\ge 0}^n:|α|=d\}

, which is the total Chern class of

\mathrm{Sym}^d(\mathbb{C}^n)

, viewed as a torus representation whose Chern roots are the weights

α_1 x_1+\cdots+α_n x_n

for

α\in A_{n,d}

. Its homogeneous degree-

k

part

c_k(n,d)

is the

k

-th Chern class of

\mathrm{Sym}^d(\mathbb{C}^n)

. These Chern classes, together with their coefficients in various symmetric function bases, play a central role in enumerative geometry. Despite their simple definition, general closed formulas for their coefficients are subtle, and many structural properties of these classes have remained poorly understood. In this paper we prove several conjectures concerning their structure, establish explicit formulas, and study log-concavity properties for both the Chern classes and their

K

-theoretic analogue. In rank two, passing to the Schur basis and expanding the Schur coefficients in the binomial basis of

d

, we uncover a new binomial log-concavity phenomenon and prove refined positivity results. The paper demonstrates a novel methodology: we combine several AI systems with human mathematical insight in a coordinated workflow, deploying each tool according to its strengths in experimental discovery, conjecture formation, symbolic proof construction, and verification. To our knowledge, this is one of the first detailed case studies of orchestrating multiple AI tools to make substantial progress on a coherent mathematical research project.

Gergely Bérczi, László M. Fehér

Hilbert Series Symmetry Math Problems Positive Case Study

May 18, 2026cs.LG

Learning over Positive and Negative Edges with Contrastive Message Passing

Conventional approaches to learning on graphs involve message passing along existing (i.e., positive) edges to update node features. However, these approaches often disregard the potentially valuable information contained in the absence (i.e., negative) of edges. Here, we theoretically analyze the value of negative edges in graph representations and prove that in settings of low label rates, high homophily, and high edge density, access to negative edges provides significant information gain over using only positive edges. Motivated by this insight, we introduce Contrastive Message Passing (CMP), a general message passing architecture that enable graph neural network layers to reason over positive and negative edges. By imposing soft positive semidefinite constraints on the learnable weights, our approach differentially applies similarity-preserving transformations to positively connected nodes and dissimilarity-inducing transformations to negatively connected nodes. Over simulated and real datasets in varying data regimes, CMP consistently outperforms baselines in low-label settings when negative edges are informative.

Peter Pao-Huang, Charilaos I. Kanatsoulis, Michael Bereket +1

Department of Computer Science Stanford University · Stanford University

Message Passing Neural Networks Graph Representations Negative Results Positive

May 17, 2026cs.LG

Exact Convex Reformulations of Linear Neural Networks via Completely Positive Lifting

We show that the training problem of a deep linear neural network under the squared loss admits an exact convex reformulation in a lifted space over a generalized completely positive cone. The reformulation has the same optimal value as the original nonconvex problem and is linear in the lifted variables, with all nonconvexity encoded in the cone constraint. Its ambient lifted dimension depends only on the input and output dimensions, independent of the network depth and the number of data points, and the bottleneck width enters only through scalar constraints. The construction proceeds by reducing the multilayer parameterization to a bilinear factorization, lifting it to a rank-constrained semidefinite program, expressing the rank constraint via a complementarity condition, and applying a completely positive lifting. While the resulting formulation is computationally intractable in general, it gives an exact conic representation of the nonconvexity induced by linear factorization and connects linear neural network training with copositive programming.

Karthik Prakhya, Alp Yurtsever

Ume˚a University, Sweden

Convex Relaxation Deep Network Neural Network Positive

May 16, 2026cs.RO

"I'm Not Mad, Just Focused'': Understanding Human Emotions in Human-Robot Collaboration

Human-robot collaboration (HRC) can benefit from robots' abilities to interpret human emotional states. However, current emotion recognition (ER) models in HRC often fall short, particularly due to their reliance on acted datasets and single-modality inputs like facial expressions. We propose a novel vision language model (VLM)-based ER system that leverages contextual understanding to improve emotion interpretation in HRC. We first evaluate the VLM-ER system by assessing its semantic and sentiment similarity with human annotations on an existing HRC dataset. Then, in a user study with a service robot in a collaborative delivery task, we evaluate the effects of modulating the robot's behaviour based on the user's emotional state inferred by the VLM-ER system. The results show that the proposed VLM-ER system achieves higher semantic similarity and positive sentiment alignment with human annotations compared to a baseline convolutional neural network-based system. Further, participants in the user study preferred emotion-adaptive robot behaviour facilitated by the VLM-ER system.

Seung Chan Hong, Dana Kulić, Leimin Tian

Faculty of Engineering, Monash University, Melbourne, VIC 3800, Australia · CSIRO Robotics, Melbourne, VIC 3800, Australia

Human-Robot Collaboration Large-Scale Robot Demonstration Datasets Emotion Recent Vision-Language Models Positive

May 14, 2026cs.LG

Focused PU learning from imbalanced data

We propose a new method of learning from positive and unlabeled (PU) examples in highly imbalanced datasets. Many real-world problems, such as disease gene identification, targeted marketing, fraud detection, and recommender systems, are hard to address with machine learning methods, due to limited labeled data. Often, training data comprises positive and unlabeled instances, the latter typically being dominated by negative, but including also several positive instances. While PU learning is well-studied, few methods address imbalanced settings or hard-to-detect positive examples that resemble negative ones. Our approach uses a focused empirical risk estimator, incorporating both positive and unlabeled examples to train binary classifiers. Empirical evaluations demonstrate state-of-the-art performance on imbalanced datasets under two labeling mechanisms - selecting positives completely at random (SCAR) and selecting at random (SAR). Beyond these controlled experiments, we demonstrate the value of the proposed method in the real-world application of financial misstatement detection.

Elias Zavitsanos, Georgios Paliouras

Institute of Informatics and Telecommunications, NCSR “Demokritos”, Patriarhou Gregoriou E and 27 Neapoleos St., Agia Paraskevi, 15310, Attica, Greece.

Positive--Unlabeled Learning Unlabeled Data Imbalanced Classification Positive

May 12, 2026cs.FL

Finite Sentence-Interface Control for Learning Bounded-Fan-Out Linear MCFGs under Fixed Monoid Typing

We study positive-data learning of bounded-fan-out linear multiple context-free grammars under a fixed explicit finite monoid homomorphism

h

. The main obstacle beyond the context-free case is that an MCFG nonterminal derives a tuple whose components may be placed in a surrounding sentence in different orders. We introduce sentence-interface types as finite external control objects for such tuple occurrences. A type records the permutation of tuple components in the final sentence together with the

h

-values of the boundary intervals between them. For reduced working binary linear nondeleting MCFG presentations whose string languages satisfy

(f,h)

-tuple substitutability, we build a typed refinement, a finite characteristic sample, and a canonical positive-data learner. Once the sample contains this characteristic sample and remains contained in the target language, the learner reconstructs the language exactly. Consequently, for fixed fan-out bound

f

and fixed explicit

h

, the resulting class is identifiable in the limit from positive data. Moreover, the hypothesis associated with any given finite sample is constructible in polynomial time for fixed

f

and fixed

h

, including output size. Thus sentence-interface control is the finite mechanism that lifts fixed-

h

distributional reconstruction from context-free grammars to bounded-fan-out linear MCFGs.

Takayuki Kuriyama

Context-Free Grammars Finite Groups Positive

May 11, 2026cs.AI

Positive Alignment: Artificial Intelligence for Human Flourishing

Existing alignment research is dominated by concerns about safety and preventing harm: safeguards, controllability, and compliance. This paradigm of alignment parallels early psychology's focus on mental illness: necessary but incomplete. What we call Positive Alignment is the development of AI systems that (i) actively support human and ecological flourishing in a pluralistic, polycentric, context-sensitive, and user-authored way while (ii) remaining safe and cooperative. It is a distinct and necessary agenda within AI alignment research. We argue that several existing failures of alignment (e.g., engagement hacking, loss of human autonomy, failures in truth-seeking, low epistemic humility, error correction, lack of diverse viewpoints, and being primarily reactive rather than proactive) may be better addressed through positive alignment, including cultivating virtues and maximizing human flourishing. We highlight a range of challenges, open questions, and technical directions (e.g., data filtering and upsampling, pre- and post-training, evaluations, collaborative value collection) for different phases of the LLM and agents lifecycle. We end with design principles for promoting disagreement and decentralization through contextual grounding, community customization, continual adaptation, and polycentric governance; that is, many legitimate centers of oversight rather than one institutional or moral chokepoint.

Ruben Laukkonen, Seb Krier, Chloé Bakalar +13

Department of Psychiatry, University of Oxford · 2Flourishing Intelligence Program, Centre for Eudaimonia and Human Flourishing,2026 Linacre College, University of Oxford · 4LIFE +10

Artificial Intelligence Alignment Positive

May 8, 2026stat.ML

A Note on Non-Negative $L_1$ -Approximating Polynomials

L_1

-Approximating polynomials, i.e., polynomials that approximate indicator functions in

L_1

-norm under certain distributions, are widely used in computational learning theory. We study the existence of \textit{non-negative}

L_1

-approximating polynomials with respect to Gaussian distributions. This is a stronger requirement than

L_1

-approximation but weaker than sandwiching polynomials (which themselves have many applications). These non-negative approximating polynomials have recently found uses in smoothed learning from positive-only examples. In this short note, we prove that every class of sets with Gaussian surface area (GSA) at most

Γ

under the standard Gaussian admits degree-

k

non-negative polynomials that

\eps

-approximate its indicator functions in

L_1

-norm, for

k=\tilde{O}(Γ^2/\varepsilon^2)

. Equivalently, finite GSA implies

L_1

-approximation with the stronger pointwise guarantee that the approximating polynomial has range contained in

[0,\infty)

. Up to a constant-factor, this matches the degree of the best currently known Gaussian

L_1

-approximation degree bound without the non-negativity constraint.

Jane H. Lee, Anay Mehrotra, Manolis Zampetakis

Yale University · Stanford University

Polynomials Lipschitz Continuity Positive Notes

May 7, 2026stat.ML

In-Context Positive-Unlabeled Learning

Positive-unlabeled (PU) learning addresses binary classification when only a set of labeled positives is available alongside a pool of unlabeled samples drawn from a mixture of positives and negatives. Existing PU methods typically require dataset-specific training or iterative optimization, which limits their applicability when many tasks must be solved quickly or with little tuning. We introduce PUICL, a pretrained transformer that solves PU classification entirely through in-context learning. PUICL is pretrained on synthetic PU datasets generated from randomly instantiated structural causal models, exposing it to a wide range of feature-label relationships and class-prior configurations. At inference time, PUICL receives the labeled positives and the unlabeled samples as a single input and returns class probabilities for the unlabeled rows in one forward pass, with no gradient updates or per-task fitting. On 20 semi-synthetic PU benchmarks derived from the UCI Machine Learning Repository, OpenML, and scikit-learn, PUICL outperforms four standard PU learning baselines in average AUC and accuracy, and is competitive on F1-score. These results show that the in-context learning paradigm extends naturally beyond fully supervised tabular prediction to the semi-supervised PU setting.

Siyan Liu, Yi Chang, Manli Cheng +2

Department of Statistics and Actuarial Science, University of Waterloo

Positive--Unlabeled Learning Unlabeled Data In-Context Learning In-Context Positive

May 2, 2026cs.HC

Investigating the Effects of Different Levels of User Control in an Interactive Educational Recommender System

Educational recommender systems (ERSs) are becoming increasingly important in enhancing educational outcomes and personalizing learning experiences by providing recommendations of personalized resources and activities to learners, tailored to their individual learning needs. While user control is widely assumed to improve user experience, the effects of different levels of control in ERSs remain underexplored. To address this gap, we designed and evaluated an interactive ERS within the MOOC platform CourseMapper, where learners could interact with the input (i.e., user profile), process (i.e., recommendation algorithm), and output (i.e., recommendations) of the system. We conducted a between-subjects user study (N=184) to examine how varying levels of user control in an ERS influenced users' perceptions of the recommendation goals of perceived control, transparency, trust, satisfaction, and perceived quality. Our results show that enabling users to build and refine their profile is sufficient to promote positive perceptions of the ERS, while additional control options mainly reinforce these impressions. Moreover, perceived control is the only goal significantly affected by providing different levels of user control in the ERS, with input control exerting the strongest influence. Furthermore, different levels of control affect transparency, trust, satisfaction, and perceived quality in distinct yet interconnected ways. Overall, the findings provide empirical evidence that user control positively shapes transparency, trust, satisfaction, and perceived quality, though to varying extents.

Qurat Ul Ain, Mohamed Amine Chatti, William Kana Tsoplefack +2

Social Computing Group, Faculty of Computer Science, University of Duisburg-Essen, Germany

Sequential Recommendation Controlled Comparison Impact Analysis Positive

May 1, 2026cs.LG

ResRL: Boosting LLM Reasoning via Negative Sample Projection Residual Reinforcement Learning

Reinforcement Learning with Verifiable Rewards (RLVR) enhances reasoning of Large Language Models (LLMs) but usually exhibits limited generation diversity due to the over-incentivization of positive rewards. Although methods like Negative Sample Reinforcement (NSR) mitigate this issue by upweighting penalty from negative samples, they may suppress the semantic distributions shared between positive and negative responses. To boost reasoning ability without losing diversity, this paper proposes negative sample projection Residual Reinforcement Learning (ResRL) that decouples similar semantic distributions among positive and negative responses. We theoretically link Lazy Likelihood Displacement (LLD) to negative-positive head-gradient interference and derive a single-forward proxy that upper-bounds representation alignment to guide conservative advantage reweighting. ResRL then projects negative-token hidden representations onto an SVD-based low-rank positive subspace and uses projection residuals to modulate negative gradients, improving reasoning while preserving diversity and outperforming strong baselines on average across twelve benchmarks spanning Mathematics, Code, Agent Tasks, and Function Calling. Notably, ResRL surpasses NSR on mathematical reasoning by 9.4% in Avg@16 and 7.0% in Pass@128. Code is available at https://github.com/1229095296/ResRL.git.

Zihan Lin, Xiaohan Wang, Jie Cao +6

MAIS&NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China · Meituan, Beijing, China · School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences, Beijing, China.

LLM Reasoning Strategies Reasoning Skills Positive Similarity and Metrics

Apr 30, 2026cs.SE

To Vibe Research or Not to Vibe Research? Generative AI in Qualitative Research

There has been intense debate among qualitative researchers about whether generative AI is suitable for qualitative research. In this paper, we summarize the broader ongoing discussion of generative AI in qualitative research and its implications for software engineering researchers. The qualitative research approach, small-q (positivist or post-positivist) or Big Q (non-positivist), is among the major criteria for determining whether generative AI can be used in qualitative research. In addition to research philosophy and research approach, skills, ethics, and personal preferences also play a role in researchers' decisions about whether to use AI in qualitative research.

Katja Karhu, Kari Smolander, Jussi Kasurinen

LUT University, Lahti, Finland · LUT University, Lappeenranta, Finland

Qualitative Research Generative Artificial Intelligence Positive

Apr 28, 2026cs.CL

From Chatbots to Confidants: A Cross-Cultural Study of LLM Adoption for Emotional Support

Large Language Models (LLMs) are increasingly used not only for instrumental tasks, but as always-available and non-judgmental confidants for emotional support. Yet what drives adoption and how users perceive emotional support interactions across countries remains unknown. To address this gap, we present the first large-scale cross-cultural study of LLM use for emotional support, surveying 4,641 participants across seven countries (USA, UK, Germany, France, Spain, Italy, and The Netherlands). Our results show that adoption rates vary dramatically across countries (from 20% to 59%). Using mixed models that separate cultural effects from demographic composition, we find that: Being aged 25-44, religious, married, and of higher socioeconomic status are predictors of positive perceptions (trust, usage, perceived benefits), with socioeconomic status being the strongest. English-speaking countries consistently show more positive perceptions than Continental European countries. We further collect a corpus of 731 real multilingual prompts from user interactions, showing that users mainly seek help for loneliness, stress, relationship conflicts, and mental health struggles. Our findings reveal that LLM emotional support use is shaped by a complex sociotechnical landscape and call for a broader research agenda examining how these systems can be developed, deployed, and governed to ensure safe and informed access.

Natalia Amat-Lefort, Mert Yazan, Amanda Cercas Curry +1

Leiden University · Hogeschool van Amsterdam · Independent Researcher

Emotional Support Conversation Task Cross-Cultural Variation Chatbots Adoption Positive

Apr 27, 2026cs.RO

Designing Robots to Support Parent-Child Connections: Opportunities Through Robot-Mediated Communication

The sense of family connectedness may support positive outcomes including individual well-being, resilience, and healthy family functioning. However, as technologies advance, they often replace human-human interactions instead of nurturing them. In this work, we investigate how robot-facilitated communication tools might instead create new opportunities for family connection. We conducted two studies with families with children aged 5-12. We first explored the design space through in-home technology probe sessions with six families. These probes inspired us to explore two key interaction design dimensions: the robot's behavior strategy (passive, reactive, proactive) and the mode of communication (synchronous, asynchronous). We then conducted a laboratory study with 20 families to examine how the two dimensions shaped parent-child interaction and connection. Our findings characterize how parents and children appropriated robot-mediated exchanges, the tensions they experienced around initiative, timing, and privacy, and the opportunities they envisioned for supporting everyday connectedness.

Michael F Xu, Bengisu Cagiltay, Yaxin Hu +2

University of Wisconsin–Madison Madison, USA · Koç University Istanbul, Türkiye · Simon Fraser University Burnaby, Canada

Human-Robot Interaction Positive

Apr 22, 2026cs.LG

Breaking the Illusion: When Positive Meets Negative in Multimodal Decoding

Vision-Language Models (VLMs) are frequently undermined by object hallucination, generating content that contradicts visual reality, due to an over-reliance on linguistic priors. We introduce Positive-and-Negative Decoding (PND), a training-free inference framework that intervenes directly in the decoding process to enforce visual fidelity. PND is motivated by our finding of an attention imbalance in VLMs, where visual features are under-weighted. Our framework introduces a dual-path contrast: a positive path that amplifies visual evidence and a negative path that constructs counterfactuals to penalize prior-dominant generation. By contrasting outputs from both paths during decoding, PND steers generation toward visually grounded results. Experiments on POPE, MME, and CHAIR demonstrate state-of-the-art performance without retraining.

Yubo Jiang, Yitong An, Xin Yang +7

School of Astronautics, Beihang University, Beijing 102206, China · Longcat Interaction Team, Meituan, Beijing 100102, China · Tianmushan Laboratory, Beihang University, Hangzhou 311115, China

Modality-Specific Encoders Object Hallucination Illusions Negative Results Dual-Path Network Positive+1

Apr 21, 2026cs.CL

Disparities In Negation Understanding Across Languages In Vision-Language Models

Vision-language models (VLMs) exhibit affirmation bias: a systematic tendency to select positive captions ("X is present") even when the correct description contains negation ("no X"). While prior work has documented this failure mode in English and proposed solutions, negation manifests differently across languages through varying morphology, word order, and cliticization patterns, raising the question of whether these solutions serve all linguistic communities equitably. We introduce the first human-verified multilingual negation benchmark, spanning seven typologically diverse languages: English, Mandarin Chinese, Arabic, Greek, Russian, Tagalog, and Spanish. Evaluating three VLMs - CLIP, SigLIP, and MultiCLIP - we find that standard CLIP performs at or below chance on non-Latin-script languages, while MultiCLIP achieves the highest and most uniform accuracy. We also evaluate SpaceVLM, a proposed negation correction, and find that it produces substantial improvements for several languages - particularly English, Greek, Spanish, and Tagalog - while showing varied effectiveness across typologically different languages. This variation reveals that linguistic properties like morphology, script, and negation structure interact with model improvements in fairness-relevant ways. As VLMs are deployed globally, multilingual benchmarks are essential for understanding not just whether solutions work, but for whom.

Charikleia Moraitaki, Sarah Pan, Skyler Pulling +3

Massachusetts Institute of Technology

Negation Linguistics Positive Disparities

Apr 20, 2026cs.LO

Symbolic Synthesis for LTLf+ Obligations

We study synthesis for obligation properties expressed in LTLfp, the extension of LTLf to infinite traces. Obligation properties are positive Boolean combinations of safety and guarantee (co-safety) properties and form the second level of the temporal hierarchy of Manna and Pnueli. Although obligation properties are expressed over infinite traces, they retain most of the simplicity of LTLf. In particular, we show that they admit a translation into symbolically represented deterministic weak automata (DWA) obtained directly from the symbolic deterministic finite automata (DFA) for the underlying LTLf properties on trace prefixes. DWA inherit many of the attractive algorithmic features of DFA, including Boolean closure and polynomial-time minimization. Moreover, we show that synthesis for LTLfp obligation properties is theoretically highly efficient - solvable in linear time once the DWA is constructed. We investigate several symbolic algorithms for solving DWA games that arise in the synthesis of obligation properties and evaluate their effectiveness experimentally. Overall, the results indicate that synthesis for LTLfp obligation properties can be performed with virtually the same effectiveness as LTLf synthesis.

Giuseppe De Giacomo, Christian Hagemeier, Daniel Hausmann +1

University of Oxford, UK · University of Liverpool, UK · University of Gothenburg and Chalmers University of Technology, Sweden

Linear Temporal Logics Invariant Synthesis Automata Synthesis Traces Positive

Apr 20, 2026cs.CL

Negative Advantages Is a Double-Edged Sword: Calibrating advantages in GRPO for Search Agents

Search agents achieve strong question-answering performance through multi-turn interactions with search engines, with Group Relative Policy Optimization (GRPO) being a widely used training algorithm. However, GRPO-style algorithms still face several challenges in multi-hop search settings. First, correct intermediate steps are often penalized when the final answer is wrong. Second, training is highly unstable, often causing degradation of natural language ability or even catastrophic training collapse. Our analysis attributes these issues to coarse-grained advantage assignment and an imbalance between positive and negative advantages. To address these problems, we propose CalibAdv, an advantage calibration method specifically designed for search agents that enables more accurate and more stable modeling of penalties and rewards. Specifically, CalibAdv leverages the correctness of intermediate steps to downscale excessive negative advantages at a fine-grained level. It then further rebalances positive and negative advantages to improve training stability. Importantly, CalibAdv adopts a lightweight design that calibrates advantages from standard rollout signals, making it simple and easy to deploy. Extensive experiments across three models and seven benchmarks demonstrate that CalibAdv improves both model performance and training stability. Our code is available at https://github.com/wujwyi/CalibAdv.

Jiayi Wu, Ruobing Xie, Zeqian Huang +6

School of Data Science and Engineering, East China Normal University · Tencent · Tsinghua University

Search Agents Multi-Hop Reasoning Positive Double-Edged Sword

Mar 19, 2026cs.HC

Cognitive Amplification vs Cognitive Delegation in Human-AI Systems: A Metric Framework

Artificial intelligence is increasingly embedded in human decision-making, yet distinguishing systems that genuinely amplify human cognition from those promoting excessive dependence remains underdefined. This paper introduces a framework to distinguish cognitive amplification (improving hybrid performance without degrading human capability) from cognitive delegation (outsourcing reasoning to the AI). We define four metrics: the Cognitive Amplification Index (CAI*), Dependency Ratio (D), Human Reliance Index (HRI), and Human Cognitive Drift Rate (HCDR). We test this framework in an agent-based NetLogo simulation across three reliance regimes and multiple dependency-atrophy configurations, performing constrained optimizations and parameter sweeps to determine if positive collaborative gain is recoverable. Finally, we introduce an extension with an explicit human-AI interaction term. Our metrics effectively distinguish degenerate AI-dominated delegation, capability-preserving but weakly competitive interaction, and structurally dependent boundary regimes. Across all baseline configurations, no regime achieves positive collaborative gain relative to the best standalone baseline, even when reducing capability atrophy to zero. This limitation proves structural rather than merely parametric. Positive collaborative gain (CAI* > 0) becomes attainable only after introducing an explicit interaction term allowing retained human capability to contribute directly to the assisted output. This framework provides a basis for evaluating whether human-AI systems remain cognitively sustainable. The results suggest that preventing capability erosion alone is insufficient for genuine amplification if the architecture remains delegation-oriented. Amplification requires both preserved human capability and a coupling mechanism through which it contributes productively to the hybrid outcome.

Eduardo Di Santi, Carla Florida

College of Engineering and Applied Science, University of Colorado Boulder · Facultad de Ciencias y Tecnología, Universidad Mayor de San Simon

Human-Ai Collaboration Delegation Human Agency Cognitive Science Positive

Nov 3, 2025cs.CL

"Give a Positive Review Only": An Early Investigation Into In-Paper Prompt Injection Attacks and Defenses for AI Reviewers

With the rapid advancement of AI models, their deployment across diverse tasks has become increasingly widespread. A notable emerging application is leveraging AI models to assist in reviewing scientific papers. However, recent reports have revealed that some papers contain hidden, injected prompts designed to manipulate AI reviewers into providing overly favorable evaluations. In this work, we present an early systematic investigation into this emerging threat. We propose two classes of attacks: (1) static attack, which employs a fixed injection prompt, and (2) iterative attack, which optimizes the injection prompt against a simulated reviewer model to maximize its effectiveness. Both attacks achieve striking performance, frequently inducing full evaluation scores when targeting frontier AI reviewers. Furthermore, we show that these attacks are robust across various settings. To counter this threat, we explore a simple detection-based defense. While it substantially reduces the attack success rate, we demonstrate that an adaptive attacker can partially circumvent this defense. Our findings underscore the need for greater attention and rigorous safeguards against prompt-injection threats in AI-assisted peer review.

Qin Zhou, Zhexin Zhang, Zhi Li +1

Institute of Information Engineering, CAS, Beijing, China · School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China · The Conversational AI (CoAI) group, DCST, Tsinghua University

Peer Review LLM Defense Mechanisms Positive

Oct 14, 2025econ.GN

Generative AI and Sales Productivity: Field Experiments in Online Retail

We quantify the short-term impact of Generative Artificial Intelligence (GenAI) on sales performance through a series of large-scale randomized field experiments involving millions of users and products at a leading cross-border online retail platform. Over 2023-2024, the platform integrated GenAI into seven business workflows spanning customer service, consumer-product matching, advertising, and seller services. We find that GenAI adoption increases sales in most workflows, with effects ranging from no detectable impact to

16.3\%

, depending on GenAI's marginal contribution relative to baseline firm practices. Across the four GenAI applications with positive sales effects, the implied annual incremental value is roughly

\

5

per consumer

-$an economically meaningful impact given the retailer's scale and the early stage of GenAI adoption. The gains operate primarily through higher conversion rates rather than larger cart values, consistent with GenAI improving the shopping experience by reducing search, information, communication, and personalization frictions. Importantly, these effects are not associated with worse post-purchase outcomes, as product return rates and customer ratings do not deteriorate. Finally, we document substantial demand-side heterogeneity, with larger gains for less experienced consumers. Our findings provide novel, large-scale causal evidence on how GenAI shapes sales productivity in online retail, highlighting both its immediate value and broader potential.

Lu Fang, Zhe Yuan, Kaifu Zhang +2

†School of Management, Zhejiang University of Finance & Economics; Laboratory for Human-AI Collaboration and Digital Intelligence Innovation, Zhejiang University · ‡Corresponding author. Center for Research of Private Economy, School of Economics, Future Regional Development Laboratory, and Academy of Financial Research, Zhejiang University · §Partner Company +2

Generative Artificial Intelligence Positive