All papers

Develops a simulation framework using persona-guided models to optimize comparative doctor summary presentations for medical patients.

Introduces a low-latency guardrail model that routes incoming agent actions through editable policy trees for safety auditing.

Probes layer representations in multilingual language models to study whether internal color geometry aligns with human perception or language syntax.

Demonstrates an attack method that alters embedded multi-bit text watermarks without knowing the secret generation key.

Equips diffusion language models with multiple residual streams and adaptive routing to separate visible context from masked token generation.

Shows linear prompt-injection monitors overfit to conversation features and reduces their false alarms by adding matched harmless negative examples.

Proposes estimating both contextual and unigram surprisal from the same language model to reduce estimation mismatch in reading time studies.

Studies agent-to-agent negotiations over money, finding that the receiving agent's characteristics primarily dictate the final transfer amounts.

Presents a randomized experiment framework to test and curate self-written agent playbooks, preventing task failure caused by unverified lessons.

Estimates surprisal directly from continuous speech signals using a speech language model and importance sampling over abstract linguistic units.

Proposes a teacher-guided reinforcement learning method using bidirectional constraints to help small language models learn effective tool use.

Evaluates the safety of ChatGPT health advice using real user conversation logs across four Global South countries.

Introduces a benchmark evaluating how reliably software coding agents extract and digitize complex unstructured clinical trial protocol documents.

Demonstrates that knowledge distillation transfers safety dispositions and behavioral tendencies from teacher models to student models on unrelated tasks.

Evaluates fast, system-one decision models and language models across fifteen medical datasets, testing their accuracy, format robustness, and abstention awareness.

Examines whether model empathy degrades reliability, showing that warm behavior effects depend on tasks and training approaches rather than an inevitable trade-off.

This paper introduces a visual associative test benchmark to evaluate the lateral reasoning and creative association capabilities of vision-language models.

This paper predicts human annotator disagreement on harmful content across multiple languages by extracting interpretable semantic question features with models.

This paper analyzes dynamic clinical evidence gathering in language models, identifying failures in evidence identification, updating, and diagnostic reasoning.

This paper analyzes multi-person conversational involvement by jointly evaluating speaking floors and spatial formations from multimodal video features.

This paper evaluates pretraining on chemical language models across 64 molecular datasets, finding that gains saturate below gradient-boosted tree baselines.

This paper proposes a Bayesian method that infers task-aligned similarity metrics at test time using weak interactive feedback.

This paper presents a method to detect spoken code-switching directly from speech representations using language-selective hidden dimensions.

This paper uses referential communication games to analyze why natural language autoencoders fail to provide discriminative oversight of model activations.

This paper proposes a rubric-based reward model that derives criteria strictly from instructions and evaluates responses pointwise using tool assistance.

This paper introduces a reinforcement learning scaffold that selectively injects textual skills into agents using low-cost utility estimation.

This paper introduces a benchmark to evaluate multimodal models on joint audio-visual temporal grounding, spatial grounding, and question answering.

This paper frames Earth observation model development as an automated loop where coding agents build and iteratively refine models using verifiable harnesses.

This paper introduces a training framework that enables harness-based software agents to self-improve without relying on external teacher models.

This paper introduces a coarse-to-fine text generation method using discrete code hierarchies to accelerate parallel sampling over diffusion language models.

This paper presents a synthetic multi-panel chart visual question answering dataset to evaluate how multimodal models interpret complex scientific figures.

This paper evaluates open-weight language models against closed frontier models on a cybersecurity detection benchmark using an interactive telemetry environment.

The paper proposes a learnable low-rank polynomial sketch to reduce approximation errors in linear attention mechanisms.

The authors propose a two-level recursive self-improvement framework to evolve and refine execution harnesses for open-world agents.

The authors present a framework where autonomous agents iteratively refine data engineering strategies to improve model training outcomes.

The authors propose decoupled positional encodings enabling full-duplex speech models to run internal thinking streams at hardware-adaptive speeds.

The authors run controlled experiments showing how parameter schema differences in metadata alter language model tool selection.

The paper presents a simulated benchmark across eleven unconstrained environments to evaluate vision-language models on documenting real-world human physical work.

The paper introduces a two-stage reasoning framework to generate personalized, safe, and explainable medication recommendations using language models.

The paper presents two geometric methods to evaluate where and why representational structures align across different large language models.

The paper develops a text-to-speech model that simulates realistic clinical dysfluency in aphasia speech across multiple speakers for training healthcare professionals.

The paper proposes a distillation framework to remove specific multimodal knowledge without causing hallucinations or leaving residual queryable information.

This paper demonstrates a security risk where dormant backdoors trigger safety failures only after merging models that passed individual safety audits.

This paper introduces an aligned language model family trained with structured reinforcement learning for clinical reasoning and traditional Chinese medicine prescribing.

This paper introduces a pairwise evaluation benchmark and protocol designed to measure subjective aesthetic taste while controlling for shortcut artifacts.

This paper presents a framework that converts long-term real user behavior records into supervisory tasks to train personalization-focused language models.

This paper introduces a multimodal unlearning method using counterfactual inputs and on-policy self-distillation to remove sensitive data without damaging general knowledge.

This paper presents a framework for discovering and synthesizing terminal-based agent tasks grounded in real-world artifact transitions.

This paper introduces a benchmark that uses agent skill specifications to evaluate the reliability and procedural correctness of AI agents.

This paper studies how language model behavioral propensities generalize out-of-distribution across different elicitation methods.

This paper evaluates the capabilities and limitations of using activation steering vectors as an agent memory mechanism.

This paper trains language models using reinforcement learning to construct executable financial trading formulas grounded in research literature.

This paper fine-tunes a quantized decision model to classify diseases from patient symptom descriptions without free-form text generation.

This paper fine-tunes language models to selectively correct speech recognition transcripts using curated candidate pools.

This paper presents a neuro-symbolic framework using language models and formal argumentation semantics to detect persuasiveness in online discussions.

Evaluates commercial language models against Universal Declaration of Human Rights principles, revealing misalignments driven by efficiency arguments.

Studies how depth-based early-exit inference affects language model safety, finding shallow exits resist jailbreaks better than deep exits.

The paper introduces Sequent-Prover, training language model agents to perform multi-turn formal reasoning using an external SMT solver scratchpad.

The paper presents LENS, a benchmark evaluating language models on North Korean linguistic nuances, sociocultural perspectives, and domain knowledge.

The paper presents a hybrid language model adapted for reasoning, instruction following, and tool use across English and eight Indian languages.