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Newest papers, page 46
Integrate the Reward, Don't Sample It: Window Likelihood for Interruptions and Backchannels in Full-Duplex Speech Models
A full-duplex spoken dialogue model must yield when the user cuts in and hold when the user merely backchannels.Linear Probes Transfer from Base to Distilled Language Models despite Rotation
Linear probes, classifiers that read a language model's internal activations, are widely used to detect harmful requests.Making Reasoning Visible: Configuration-Aware Diagnostic Modeling for Adaptive Scaffolding in Structured Reasoning Tasks
Modeling the latent, multi-step reasoning processes of learners is a core challenge for adaptive tutoring systems, which often rely on simple right/wrong answers rather than understanding how…Hierarchical Skill Evolution for Coding Agent
Skills provide agents with reusable experience to guide future decisions without modifying the underlying model.Improving JEV Entailment Reranking with Span Readouts and Selective Adaptation
JEV decision models pair a language model backbone with a lightweight scoring head to select among natural-language options.Evaluating DeepSeek V4.1 Flash-1017 for loss of control and harmful manipulation risks
As frontier language models grow increasingly capable and autonomous, independent third-party evaluations are critical for characterizing their systemic safety risks.Adapting LLM-SC to Living Sacred Heritage: Domain-Specific Extraction, Semantic Networks and Human Evaluation on Pashupatinath Temple Reviews
Large language model (LLM) pipelines can turn visitor reviews into structured data, but generic schemes may miss rituals, sacred spaces, cultural restrictions and emotionally mixed experiences at…Not One Belief: Answer Payloads, Persistence, and Dissociated Confidence Signals in Conversational Question Answering
Conversational QA systems should fix errors when users push back and signal which answers to trust.MiRL: Error-Targeted Reinforcement Learning for Correcting Terminal Agent Misbehaviors
Error-targeted learning gives a legible way to improve a model: identify specific weaknesses of the model, train the model to fix these weaknesses, and check whether the weaknesses are fixed.From Self-Confidence to Statistical Accuracy: Scaling Human-in-the-Loop Evaluation with Direct and LLM-Assisted Auditing
For large language models (LLMs) and multimodal large language models (MLLMs), model-reported self-confidence is often miscalibrated, yet it may still be useful for efficient evaluation.Quantitative Generalization Bounds for Multi-Layer Transformers: A Few Long Strings can Beat Many Short Ones
Transformer-based language models exhibit an astonishing capacity to generalize to longer and more difficult problem instances than those seen in training.Can JEV Support Clinical Trial Criteria Assessment?
Clinical trial prescreening requires distinguishing unmet conditions from insufficient evidence in patient records.SEMA-Auto: Sighted and Efficient Multi-Turn Attack with Autonomy
Existing jailbreak attackers fall into two classes: static planners fix their single- or multi-turn adversarial plans in advance, remaining simple but losing sight of the victim's replies, wasting…Atomic Security Concerns: Towards a Foundational Language for Security Knowledge Representation
Cybersecurity governance, risk management, compliance, and security architecture rely on numerous concepts, including security properties, domains, controls, risks, implementation measures…Quantifying Claim Smoothing by LLM Research Agents
Deep research, or any use of large language models (LLMs) to read a large document collection and produce insights, requires synthesizing information from different sources.Learning When to Commit: Schedule Distillation for Fast Diffusion Language Models
Diffusion large language models (dLLMs) can generate multiple tokens in parallel, but often require many decoding rounds to complete a response.Understanding Jev-Style System One Models: Does Structure Reduce Selection Bias?
Selection bias in large language models (LLMs) can persist despite instructions to ignore presentation order or prompts that elicit reasoning.Preserve to Expand: Source-Aware Adaptation and Model Merging for Multilingual LLM-Based ASR
Expanding multilingual ASR systems to new languages is challenging when adaptation must preserve previously supported languages without replaying source-language training data.Center-of-Mass Interpretation of Evolution Strategies
Evolution strategies (ES) update a model by sampling a population of perturbed copies, scoring each one, and moving in the reward-weighted direction of the perturbations.MedJev: Evaluating Zero-Shot System-One Decision Making in Medicine
System One decision models such as Jev answer structured questions in a single forward pass without generating text, offering fast and computationally efficient clinical decision support.