Language Model Probing

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19 papers in the last four weeks, up 12% on the four weeks before. 0.2% of all new papers.

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Latest papers 192

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Tool Calling is Linearly Readable and Steerable in Language Models
May 8, 2026Zekun Wu, Ze Wang, Seonglae Cho +4AI Agent Reliability Tool-Augmented Language Model Agents
University College London · Holistic AI · Imperial College London
The Granularity Axis: A Micro-to-Macro Latent Direction for Social Roles in Language Models
May 7, 2026Chonghan Qin, Xiachong Feng, Ziyun Song +3LLM Interpretability Language Model Probing
The University of Hong Kong · Harbin Institute of Technology
When Does a Language Model Commit? A Finite-Answer Theory of Pre-Verbalization Commitment
May 7, 2026Long Zhang, Wei-neng Chen, Feng-feng Wei +1LLM Interpretability Language Model Probing
School of Computer Science and Engineering South China University of Technology Guangzhou City, Guangdong Province, China
HyperLens: Quantifying Cognitive Effort in LLMs with Fine-grained Confidence Trajectory
May 7, 2026Chengda Lu, Xiaoyu Fan, Wei XuConfidence Estimation in Language Models LLM Interpretability
1IIIS, Tsinghua University · 2Shanghai Qi Zhi Institute.
Implicit Representations of Grammaticality in Language Models
May 6, 2026Yingshan Susan Wang, Linlu Qiu, Zhaofeng Wu +2Language Model Probing
Massachusetts Institute of Technology
Probing Structural Mathematical Reasoning in Language Models with Algebraic Trapdoors
May 5, 2026Igor RivinMathematical Reasoning Benchmarks Mathematical Reasoning
Igor Rivin
Retrieval and competition: how a protein foundation model starts a protein
May 5, 2026Piotr Jedryszek, Oliver M. CrookProtein Language Models Mechanistic Interpretability
Department of Biology, University of Oxford, Oxford, UK · Kavli Institute for Nanoscience Discovery, University of Oxford, Oxford, UK · Department of Chemistry, University of Oxford, Oxford, UK
Geometric Deviation as an Unsupervised Pre-Generation Reliability Signal: Probing LLM Representations for Answerability
May 4, 2026Yucheng DuRepresentation Geometry LLM Reliability
University of Southern California
Beyond Decodability: Reconstructing Language Model Representations with an Encoding Probe
May 1, 2026Gaofei Shen, Martijn Bentum, Tomas O. Lentz +2Transformer Interpretability Representation Probing
Tilburg University · Radboud University
Perturbation Probing: A Two-Pass-per-Prompt Diagnostic for FFN Behavioral Circuits in Aligned LLMs
Apr 30, 2026Hongliang Liu, Tung-Ling Li, Yuhao WuLLM Interpretability Mechanistic Interpretability
Palo Alto Networks
Do LLMs Capture Embodied Cognition and Cultural Variation? Cross-Linguistic Evidence from Demonstratives
Apr 28, 2026Yu Wang, Emmanuele Chersoni, Chu-Ren HuangMultilingual Language Model Evaluation LLM Grounding
Department of Language Science and Technology · The Hong Kong Polytechnic University · Hung Lok Road, Hung Hom, Kowloon, Hong Kong (China)
Evaluation without Generation: Non-Generative Assessment of Harmful Model Specialization with Applications to CSAM
Apr 28, 2026Vinith M. Suriyakumar, Ayush Sekhari, Lena Stempfle +5LLM Auditing Language Model Probing
MIT · BU · Thorn
Incompressible Knowledge Probes: Estimating Black-Box LLM Parameter Counts via Factual Capacity
Apr 27, 2026Bojie LiParameter Estimation LLM Evaluation
Pine AI
The Pragmatic Persona: Discovering LLM Persona through Bridging Inference
Apr 27, 2026Jisoo Yang, Jongwon Ryu, Minuk Ma +2Personality Modeling in Language Models Language Model Probing
Department of Artificial Intelligence, Chung-Ang University, Seoul, 06974, Republic of Korea · Department of Computer Science, University of British Columbia,2026 Vancouver, BC V6T 1Z4, Canada · Van Lang University, Ho Chi Minh City, Vietnam
Revisiting Non-Verbatim Memorization in Large Language Models: The Role of Entity Surface Forms
Apr 23, 2026Yuto Nishida, Naoki Shikoda, Yosuke Kishinami +4LLM Evaluation Memorization in Language Models
Nara Institute of Science and Technology · Future Corporation
Measuring Opinion Bias and Sycophancy via LLM-based Persuasion
Apr 23, 2026Rodrigo Nogueira, Giovana Kerche Bonás, Thales Sales Almeida +7LLM Sycophancy LLM Auditing
Maritaca AI · JusBrasil
Slot Machines: How LLMs Keep Track of Multiple Entities
Apr 22, 2026Paul C. Bogdan, Jack LindseyLLM Interpretability Language Model Probing
Anthropic Fellows Program · Anthropic
Tracing Relational Knowledge Recall in Large Language Models
Apr 21, 2026Nicholas Popovič, Michael FärberLinear Probing Factual Knowledge in Language Models
TU Dresden & ScaDS.AI Dresden/Leipzig, Germany
Harmful Intent as a Geometrically Recoverable Feature of LLM Residual Streams
Apr 20, 2026Isaac Llorente-SaguerLanguage Model Safety Evaluation LLM Interpretability
Independent Researcher
Probing for Reading Times
Apr 20, 2026Eleftheria Tsipidi, Samuel Kiegeland, Francesco Ignazio Re +4Computational Psycholinguistics Language Model Probing
ETH Zürich · Toyota Technological Institute at Chicago · University College London
Dual Alignment Between Language Model Layers and Human Sentence Processing
Apr 20, 2026Tatsuki Kuribayashi, Alex Warstadt, Yohei Oseki +1Language Modeling Computational Psycholinguistics
MBZUAI · Tohoku University · UC San Diego +2
Exploring Concreteness Through a Figurative Lens
Apr 20, 2026Saptarshi Ghosh, Tianyu JiangFigurative Language Understanding LLM Interpretability
University of Cincinnati
STaD: Scaffolded Task Design for Identifying Compositional Skill Gaps in LLMs
Apr 20, 2026Sungeun An, Swanand Ravindra Kadhe, Shailja Thakur +2LLM Evaluation Reasoning Evaluation
IBM Research
SPENCE: A Syntactic Probe for Detecting Contamination in NL2SQL Benchmarks
Apr 20, 2026Mohammadtaher Safarzadeh, Hitesh Laxmichand Patel, Afshin Orojlooyjadid +2LLM Evaluation Benchmark Contamination
Oracle AI
Before You Interpret the Profile: Validity Scaling for LLM Metacognitive Self-Report
Apr 20, 2026Jon-Paul CacioliPsychometric Validation LLM Uncertainty Estimation
Independent Researcher, Melbourne, Australia
Introspection Adapters: Training LLMs to Report Their Learned Behaviors
Apr 18, 2026Keshav Shenoy, Li Yang, Abhay Sheshadri +4LLM Auditing Language Model Introspection
Anthropic Fellows · Ashwood Centre on AI Science and Policy, University of Cambridge · Anthropic
Segment-Level Coherence for Robust Harmful Intent Probing in LLMs
Apr 16, 2026Xuanli He, Bilgehan Sel, Faizan Ali +3LLM Safety LLM Jailbreak Attacks
University College London · Virginia Tech · Anthropic
What are They Thinking? Delineation, Probing, and Tracking of Concepts in LLMs
Apr 7, 2026Mohamed Abdelwahab, Michelle Yu Collins, Sihan Chen +5Linear Probing LLM Interpretability
The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto
GRADE: Probing Knowledge Gaps in LLMs through Gradient Subspace Dynamics
Apr 3, 2026Yujing Wang, Yuanbang Liang, Yukun Lai +2LLM Interpretability LLM Uncertainty Estimation
Beihang University · Cardiff University · King’s College London
ReLope: From Hidden-State Probing to a Decision Module for Multimodal LLM Routing
Mar 25, 2026Yaopei Zeng, Congchao Wang, Blake JianHang Chen +1Multimodal Large Language Models LLM Routing
Pennsylvania State University · Google · Amazon AGI

May 8, 2026, cs.CLJ/K move · Enter open · S save

Tool Calling is Linearly Readable and Steerable in Language Models

Zekun Wu, Ze Wang, Seonglae Cho, Yufei Yang+3

University College London · Holistic AI · Imperial College London

AI Agent Reliability Tool-Augmented Language Model Agents Language Model Introspection LLM Tool Use Tool Selection Causal Interventions in Language Models+1

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When a tool-calling agent picks the wrong tool, the failure is invisible until execution: the email gets sent, the meeting gets missed. As agents take on consequential actions, one bad tool call can do real damage. We currently have no way to look inside the model and catch the mistake before it happens; this paper shows that we can. Inside the model, the choice of tool is carried by a single direction in activation space, one direction per pair of tools. Adding that direction during generation switches which tool the model picks. Across 12 instruction-tuned and 6 base models spanning Gemma 3, Qwen 3, Qwen 2.5, and Llama 3.1 (270M to 27B), this works at 83-100% accuracy on 4B+ instruction-tuned models on a 15-tool synthetic benchmark and at 77-94% on the real-API benchmark

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-bench airline. The JSON arguments that follow automatically adapt to the new tool's schema, so flipping the name is enough. The same per-tool directions also flag likely errors before they happen: queries where the model is unsure between two tools fail 21x more often than queries where it is not (Gemma 3 27B). This is not just topic injection: random vectors at the same magnitude give a 0% switch rate, and a probe within a single domain (14 airline tools that share one topic) still reads which tool the model will call at top-1 61-89% across five 4B-14B models. Even base models already carry the right tool internally before they can emit it: reading the chosen tool off the model's internal state (cosine readout) recovers 61-82% accuracy on BFCL while base generation lands at 2-10%, suggesting pretraining forms the representation and instruction tuning later wires it to the output. Our results cover single-turn, fixed-menu settings; on multi-turn agent loops the same intervention is less stable (matched-baseline gain or loss of up to 30 percentage points with no consistent direction).

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