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Newest papers, page 69
Behind Aggregate Scores: What Systematically Varying Arena-Hard's LLM Judge Reveals
LLM judges are used to determine which of two answers is better.Beyond End-to-End Recovery: Towards a Better Understanding of LLM Steganography via Rolewise Decomposition
In large language model (LLM) steganography, an encoder hides a private message in an otherwise ordinary response, as text transmitted to a decoder, who attempts to recover it.Protegrity-PII Technical Report
We present Protegrity-PII, a new PII decision model for structured and unstructured text.IFC-MCQ: Automatically Generating Multiple-Choice Benchmarks for Evaluating LLM Agents on IFC Data Retrieval
Industry Foundation Classes (IFC), the standard for building information exchange in architecture, engineering, and construction (AEC), is increasingly queried through large language models (LLMs)…The Router Already Knows
Mixture-of-experts (MoE) models already make a sparse choice for every token: which experts should process it.Harness Self-Evolution for Long-Horizon Terminal Agents
Long-horizon terminal agents may solve a task in one run yet fail across repeated runs.Ask to Distill: Learning to Synthesize Data for On-Policy Distillation
On-policy distillation (OPD) can succeed with surprisingly few training questions, motivating a view of questions as carriers of distributional knowledge transfer.Detecting Errors in Large Language Model Responses: The Incremental Predictive Value of Hidden States
Although large language models support a wide range of tasks, hallucinations remain a major obstacle to user trust.Recoverability and Verifier Strength: Two Measured Preconditions for Unattended Agent Self-Improvement
Agent platforms are starting to improve their own agents: they read a deployed agent's execution traces, derive a change, have an automated verifier approve it, and release it with no human author.Self-Evolving Agents through Privileged Retrospective Distillation
Self-evolving agents aim to improve from their own experience rather than remain fixed after deployment, but naturally collected trajectories often lack ground-truth solutions or verifier-derived…Judging the Justification: Decision Certificate Supervision for Generative Reward Models
Generative reward models (GenRMs) generate natural language rationales to justify preference judgments, yet a correct verdict does not guarantee a sound justification.EviDial: When Correct Depression Predictions Lack Evidence
Depression dialogue benchmarks commonly assess models by comparing predicted symptom labels with clinical scale scores.Can Native Safety Travel? Reusing Safety Pretraining Across Models via On-Policy Distillation
Safety pretraining has emerged as a promising paradigm for embedding safety deeply into a model during pretraining, yielding robustness to subsequent fine-tuning that conventional safety…Exploring Recurrent-Depth Language Models for Multitask Clinical Text Processing
Clinical natural language processing includes diverse tasks such as information extraction, classification, and question answering.AnomalyJev: Towards Evaluating Image Anomaly Explanations via Claim-Level Verification
Recent multimodal LLM (MLLM)-based anomaly detection (AD) methods can generate textual explanations that help interpret their predictions.Learned Readout Tokens for Extracting Large Language Model Confidence
Large language models (LLMs) are increasingly used within everyday applications where their responses have significant downstream impacts.AudiPsy: Evidence-Grounded Multimodal Reasoning over Spoken Counseling Conversations for Depression Assessment
Depression assessment from counseling speech requires integrating textual and acoustic psychological evidence across turns, yet existing end-to-end classification approaches lack explicit multimodal…When LLMs Manipulate: Measuring Behavioral, Social, and Temporal Effects
Large language models (LLMs) can influence user trust and decision-making through subtle linguistic cues, yet the behavioral effects of manipulative outputs remain poorly quantified.Why Static Probes Fail: Context Determines Deception Feature Composition
With increasing autonomy, Large Language Models (LLMs) may engage in deceptive behaviors, such as scheming to pursue misaligned goals or sandbagging to escape oversight.Factual Access States in Diffusion Language Models: From Mechanism to Knowledge Editing and Retrieval
Factual recall is a lookup: some representation inside the network serves as the key, and what that key is made of decides which queries can retrieve the fact, what an edit must cover, and what a…