Large Language Model Performance

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Period ending 2026-09-21

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Latest in Large Language Model Performance

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The Grounding Gap: How LLMs Anchor the Meaning of Abstract Concepts Differently from Humans
May 9, 2026Odysseas S. Chlapanis, Orfeas Menis Mastromichalakis, Christos H. PapadimitriouLarge Language Model Performance Contextual Grounding
Bias by Necessity: Impossibility Theorems for Sequential Processing with Convergent AI and Human Validation
May 9, 2026Jikun Wu, Dongxin Guo, Siu-Ming YiuImplicit Bias Cognitive Science
A Single Layer to Explain Them All:Understanding Massive Activations in Large Language Models
May 8, 2026Zeru Shi, Zhenting Wang, Fan Yang +2Model Activations Large Models
Do Benchmarks Underestimate LLM Performance? Evaluating Hallucination Detection With LLM-First Human-Adjudicated Assessment
May 8, 2026I. F. Atasoy, B. Mutlu, E. A. Sezer +1Hallucination Detection Large Language Model Performance
LLMs Improving LLMs: Agentic Discovery for Test-Time Scaling
May 8, 2026Tong Zheng, Haolin Liu, Chengsong Huang +10Test-Time Scaling Large Models
Post-training makes large language models less human-like
May 8, 2026Marcel Binz, Elif Akata, Abdullah Almaatouq +76Large Language Model Post-Training Large Language Model Performance
Why do Large Language Models Fail in Low-resource Translation? Unraveling the Token Dynamics of Large Language Models for Machine Translation
May 8, 2026Shenbin Qian, Yves ScherrerNeural Machine Translation Models Machine Translation Quality
Understanding Performance Collapse in Layer-Pruned Large Language Models via Decision Representation Transitions
May 8, 2026Boyu Shi, Chang Liu, ChuanBao Gao +2Large Language Model Performance Heterogeneous Pruning Scores
The Text Uncanny Valley: Non-Monotonic Performance Degradation in LLM Information Retrieval
May 8, 2026Zekai Tong, Ruiyao Xu, Aryan Shrivastava +2Large Language Model Performance Transitions
How Well Do LLMs Perform on the Simplest Long-Chain Reasoning Tasks: An Empirical Study on the Equivalence Class Problem
May 7, 2026Chun Zheng, Lianlong Wu, Bingqian Li +2Large Language Model Reasoning Large Reasoning Models
Systematic Evaluation of Large Language Models for Post-Discharge Clinical Action Extraction
May 7, 2026Shivali Dalmia, Ananya Mantravadi, Prasanna DesikanClinical Reasoning Large Language Model Performance
Hypothesis generation and updating in large language models
May 7, 2026Hua-Dong XiongHypothesis Generation Large Language Model Performance
Decodable but Not Corrected by Fixed Residual-Stream Linear Steering: Evidence from Medical LLM Failure Regimes
May 7, 2026Ming LiuLarge Language Model Performance Mmlu-Pro
I'm Sorry, but I Can't Help with Braille: Revealing Accessibility Failures in State-of-the-Art LLMs
May 7, 2026Abdullah AbdullahLarge Language Model Performance Large Language Model Baselines
On the Hardness of Junking LLMs
May 6, 2026Marco Rando, Samuel VaiterLarge Language Model Jailbreaks Jailbreak Attacks
Paraphrase-Induced Output-Mode Collapse: When LLMs Break Character Under Semantically Equivalent Inputs
May 6, 2026Aofan Liu, Jingxiang MengLarge Language Model Performance Representation Collapse
Power Distribution Bridges Sampling, Self-Reward RL, and Self-Distillation
May 6, 2026Akiyoshi Tomihari, Issei SatoUnsupervised On-Policy Self-Distillation Large Language Model Performance
A Dialogue-Based Framework for Correcting Multimodal Errors in AI-Assisted STEM Education
May 5, 2026Akshay Syal, Lawrence Swaminathan Xavier Prince, Evin Gultepe +2Tutors Large Language Model Performance
Exposing LLM Safety Gaps Through Mathematical Encoding:New Attacks and Systematic Analysis
May 5, 2026Haoyu Zhang, Mohammad Zandsalimy, Shanu SushmitaLarge Language Model Safety Large Language Model Performance
Revisiting the Travel Planning Capabilities of Large Language Models
May 5, 2026Bo-Wen Zhang, Jin Ye, Peng-Yu Hua +4Large Language Model Planning Large Language Model Reasoning
How Language Models Process Negation
May 4, 2026Zhejian Zhou, Tianyi Zhou, Robin Jia +1Negation Large Language Model Performance
Reasoning emerges from constrained inference manifolds in large language models
May 2, 2026Yanbiao Ma, Fei Luo, Linfeng Zhang +10Large Language Model Reasoning Large Language Model Performance
When Embedding-Based Defenses Fail: Rethinking Safety in LLM-Based Multi-Agent Systems
May 1, 2026Lingxi Zhang, Guangtao Zheng, Hanjie ChenMulti-Agent Systems Model-Agnostic Defense
When LLMs Stop Following Steps: A Diagnostic Study of Procedural Execution in Language Models
May 1, 2026Sailesh Panda, Pritam Kadasi, Abhishek Upperwal +1Large Language Model Performance Reasoning Benchmark
Seeking Information with RAG-Assistants: Does Model Size Matter in Human-AI Collaborations?
May 1, 2026Lennard C. Froma, Tom Kouwenhoven, Maaike H. T. de Boer +2Human-Ai Collaboration Large Language Model Performance
The Power of Order: Fooling LLMs with Adversarial Table Permutations
May 1, 2026Xinshuai Dong, Haifeng Chen, Xuyuan Liu +5Language Model Evasion Attacks Large Language Model Performance
Why Do LLMs Struggle in Strategic Play? Broken Links Between Observations, Beliefs, and Actions
Apr 30, 2026Jan Sobotka, Mustafa O. Karabag, Ufuk TopcuLarge Language Model Decision-Making Large Language Model Performance
Investigating LLM's Problem Solving Capability -- a Study on Statics Questions
Apr 30, 2026Tanner Culleton, Hung-Fu ChangLarge Language Model Performance Engineering
Exploration Hacking: Can LLMs Learn to Resist RL Training?
Apr 30, 2026Eyon Jang, Damon Falck, Joschka Braun +6Large Language Model Performance Hacking
When Your LLM Reaches End-of-Life: A Framework for Confident Model Migration in Production Systems
Apr 29, 2026Emma Casey, David Roberts, David Sim +1Large Language Model Evaluation Large Language Model Performance
From Architecture to Output: Structural Origins of Hallucination in Large Language Models and the Amplifying Role of Data
Apr 29, 2026Md. Rejaul Korim Sadi, Toufiqur Rahman Tasin, Golam Mostofa NaeemLarge Language Model Hallucination Hallucinations
Beyond Accuracy: LLM Variability in Evidence Screening for Software Engineering SLRs
Apr 29, 2026Gilberto Sussumu Hida, Danilo Monteiro Ribeiro, Erika YahataSystematic Review Large Language Model Performance
Why Are Some Emotions Harder for LLMs? Uncovering the Causal Mechanisms of Emotion Inference via Sparse Autoencoders
Apr 28, 2026Bangzhao Shu, Arinjay Singh, Mai ElSheriefEmotion Large Language Model Performance
The Chameleon's Limit: Investigating Persona Collapse and Homogenization in Large Language Models
Apr 27, 2026Yunze Xiao, Vivienne J. Zhang, Chenghao Yang +3Personality Large Language Model Performance
How LLMs Fail and Generalize in RTL Coding for Hardware Design?
Apr 26, 2026Guan-Ting Liu, Chao-Han Huck Yang, Chenhui Deng +3Hardware Description Languages Register Transfer Level
When Corrective Hints Hurt: Prompt Design in Reasoner-Guided Repair of LLM Overcaution on Entailed Negations under OWL2DL
Apr 25, 2026Yijiashun Qi, Xiang Xu, Yuxuan LiLarge Language Model Performance Negation
Don't Make the LLM Read the Graph: Make the Graph Think
Apr 24, 2026Yuqi Sun, Tianqin Meng, George Liu +4Multi-Agent Reasoning Reasoning Graphs
DeepImagine: Learning Biomedical Reasoning via Successive Counterfactual Imagining
Apr 24, 2026Youze Zheng, Jianyou Wang, Yuhan Chen +8Large Language Model Reasoning Large Language Model Performance
A Systematic Approach for Large Language Models Debugging
Apr 24, 2026Basel Shbita, Anna Lisa Gentile, Bing Zhang +10Large Language Model Performance Llm-Driven
Multilingual Refusal Alignment for Safer Large Language Models
Apr 24, 2026Aleksandra Krasnodębska, Wojciech Kusa, Aldo LipaniMultilingual Safety Cross-Lingual Alignment
Large Language Models Outperform Humans in Fraud Detection and Resistance to Motivated Investor Pressure
Apr 22, 2026Nattavudh PowdthaveeFraud Detection Large Language Model Performance
Where Reasoning Breaks: Logic-Aware Path Selection by Controlling Logical Connectives in LLMs Reasoning Chains
Apr 22, 2026Seunghyun Park, Yuanyuan LeiLarge Language Model Reasoning Reasoning Chain
Cross-Dataset Bloom Question Classification: Supervised Models and Prompted LLMs
Apr 22, 2026Abdolali Faraji, Mohammadreza Molavi, Zohreh Rasoulkhani +2Computerized Adaptive Testing Education
IndiaFinBench: An Evaluation Benchmark for Large Language Model Performance on Indian Financial Regulatory Text
Apr 21, 2026Rajveer Singh PallFinance Benchmarks Legal Natural Language Processing Benchmarks
Heterogeneity in Formal Linguistic Competence of Language Models: Is Data the Real Bottleneck?
Apr 20, 2026H S V N S Kowndinya Renduchintala, Sumit BhatiaLarge Language Model Performance Linguistics
Do LLMs Need to See Everything? A Benchmark and Study of Failures in LLM-driven Smartphone Automation using Screentext vs. Screenshots
Apr 20, 2026Shiquan Zhang, Tianyi Zhang, Le Fang +3Large Language Model Performance Device
Contrastive Attribution in the Wild: An Interpretability Analysis of LLM Failures on Realistic Benchmarks
Apr 20, 2026Rongyuan Tan, Jue Zhang, Zhuozhao Li +3Large Language Model Performance Model Interpretability
Measuring Distribution Shift in User Prompts and Its Effects on LLM Performance
Apr 19, 2026Parker Seegmiller, Sarah Masud PreumLarge Language Model Performance Large Language Model Reliability
Evaluating Hallucinations in Domain-Adapted Large Language Models
Apr 19, 2026Sanchita Porwal, Sai Prasath S, Xingjian Bi +1Large Language Model Hallucination Hallucination Rate
Please refuse to answer me! Mitigating Over-Refusal in Large Language Models via Adaptive Contrastive Decoding
Apr 18, 2026Yupeng Qi, Ziyu Lyu, Lixin Cui +2Refusals Large Language Model Safety
Evaluating Multimodal LLMs for Inpatient Diagnosis: Real-World Performance, Safety, and Cost Across Ten Frontier Models
Apr 18, 2026Bruce A. Bassett, Amy Rouillard, Sitwala Mundia +8Multimodal Clinical Data Large Language Model Performance
Artificial Effort
Apr 17, 2026Federico Belotti, Stefano Coniglio, Antonio Cosma +1Large Language Model Performance Real-World Tasks
Disentangling Mathematical Reasoning in LLMs: A Methodological Investigation of Internal Mechanisms
Apr 17, 2026Tanja Baeumel, Josef van Genabith, Simon OstermannLarge Language Model Reasoning Mathematical Reasoning
LLMs Corrupt Your Documents When You Delegate
Apr 17, 2026Philippe Laban, Tobias Schnabel, Jennifer NevilleLarge Language Model Performance Workflow Automation and Optimization
LLM attribution analysis across different fine-tuning strategies and model scales for automated code compliance
Apr 16, 2026Jack Wei Lun Shi, Minghao Dang, Wawan Solihin +1Large Language Model Performance Code Quality
Adaptive Test-Time Compute Allocation for Reasoning LLMs via Constrained Policy Optimization
Apr 16, 2026Zhiyuan Zhai, Bingcong Li, Bingnan Xiao +2Test-Time Scaling Large Language Model Reasoning
The LLM Fallacy: Misattribution in AI-Assisted Cognitive Workflows
Apr 16, 2026Hyunwoo Kim, Harin Yu, Hanau YiArtificial Intelligence Literacy Human-Ai Collaboration
Dissecting Failure Dynamics in Large Language Model Reasoning
Apr 16, 2026Wei Zhu, Jian Zhang, Lixing Yu +2Reasoning Failures Large Language Model Reasoning
JFTA-Bench: Evaluate LLM's Ability of Tracking and Analyzing Malfunctions Using Fault Trees
Mar 24, 2026Yuhui Wang, Zhixiong Yang, Ming Zhang +10Fault Diagnosis Large Language Model Performance
Beyond Memorization: Distinguishing Between Pattern-Based and Epistemic Reasoning in LLMs Using Epistemic Puzzles
Mar 22, 2026Adi Gabay, Gabriel Stanovsky, Liat PeterfreundLarge Language Model Reasoning Epistemic Actions

May 9, 2026 · cs.CLJ/K move · Enter open · S save

The Grounding Gap: How LLMs Anchor the Meaning of Abstract Concepts Differently from Humans

Odysseas S. Chlapanis, Orfeas Menis Mastromichalakis, Christos H. Papadimitriou

Abstract concepts - justice, theory, availability - have no single perceivable referent; in the human brain, their meaning emerges from a web of experiences, affect, and social context. Do large language models (LLMs) ground abstract concepts in a similar way? We study this by replicating property-generation experiments from cognitive science on 21 frontier and open-weight LLMs. Across models and experiments, we find a consistent pattern: when compared to humans, models rely too heavily on word associations, and underproduce properties tied to emotion and internal states. This yields a large and consistent grounding gap: no model exceeds a Pearson correlation r=0.37 with human responses, compared to a human-to-human ceiling above r=0.9. To better interpret this gap, we also replicate a rating experiment on grounding categories and find that here LLMs align more closely with human judgment, and alignment improves as models get larger. We then use sparse autoencoders (SAEs) to inspect whether this information is also reflected in the models' internal features, and we do identify features connected to grounding dimensions such as "sensorimotor" and "social". These findings suggest that current LLMs can recover grounding dimensions when explicitly queried, but do not recruit them in a human-like way when words are generated freely.

Large Language Model Performance Contextual Grounding Cognitive Science Vocabulary Expansion Human Emotion Perceptual Understanding

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