All papers

The paper compares typed probabilistic decision models with large language models on tabular data classification and format robustness.

The paper presents an automated framework to predict suspected drug overdose deaths from investigation reports using expert-guided rationales.

The paper introduces a benchmark to measure how tool-calling agents perform when interacting with emotionally varied, impatient users.

The paper introduces a training-free controller that uses token entropy to target and regenerate faulty reasoning steps at test time.

The paper proposes a likelihood-based attribution method to identify the source model of machine-generated text under distribution shifts.

The paper learns trajectory embeddings to predict the success or failure of language model agents early during execution.

The paper proposes a Transformer architecture that plans dynamic cross-layer connections globally across the model based on input tokens.

The paper uses sparse autoencoders to establish a shared coordinate system for matching symbols across heterogeneous neuro-symbolic systems.

The paper proposes a decision-guided co-training method that selectively filters observation units to improve world modeling for language model agents.

The paper simulates student behavior by modeling individual learner history and constraining simulated actions via a third-person prediction harness.

The paper measures whether requester users can understand multi-step agent replies and evaluates how explicit constraints improve reply usability.

The paper proposes a graph-based task synthesis framework to help long-horizon agents satisfy inter-dependent constraints and achieve complete task outcomes.

The paper introduces a single-policy optimization framework combining privileged adaptation with stored historical next-token distributions for language model reasoning.

The paper introduces prompt optimization and recovery measurement tools to evaluate the limits of transmitting concepts between scaling language models.

This paper introduces a task and benchmark for tracking candidate sets of referent objects across ambiguous interactive dialogue turns.

This paper presents a training-free contrastive decoding framework using attention-guided perturbations to reduce hallucinations in video language models.

This paper investigates using Classical Chinese as a compact intermediate reasoning register to reduce token length in Chinese language models.

This paper proposes a multi-agent system designed to reduce clinical medication errors through dynamic collaborative coordination.

This paper trains language models using reinforcement learning to generate explicit perception-to-action rules for theory-of-mind reasoning tasks.

This paper presents a flow matching method to distill diverse continuous latent reasoning distributions from multiple chain-of-thought rationales.

The paper proposes weighting majority voting responses using a length-conditioned prior adapted at test time without model fine-tuning.

The paper investigates whether machine translation systems preserve or neutralize media bias across languages.

The paper proposes a tree search method that optimizes agent skills through contrastive atomic rule editing and execution memory.

The paper evaluates harnesses for legislative document conversion, showing structured multi-agent pipelines narrow performance gaps between models.

The paper identifies an activation steering vector that controls whether language models rely on internal memory or retrieved external context.

The paper compares three textual interface designs for optimizing prompts in multimodal audio-visual question answering systems.

The paper proposes a synthetic data pipeline that extracts atomic facts from source texts to guide factual pretraining for language models.

The paper describes an automated system using language models to verify corporate loan applications against internal bank lending policies.

The paper introduces a benchmark to evaluate conversational floor control, turn completion, and interruptions in multi-party spoken dialogue systems.

The paper uses in-context neurofeedback sessions to evaluate whether language models can intentionally control their internal activations.

The paper presents a benchmark evaluating whether conversational agents agree on verifiable plans that satisfy both parties' private conditions.

The paper introduces a reasoning-guided translation framework to scale multilingual safety and reasoning benchmarks while preserving original task meanings.

The paper introduces a benchmark and evaluation framework assessing context-sensitive code-switching by language models in creative writing tasks.

The paper proposes a framework where an agent policy navigates a global search tree by alternating between reflection and execution roles.

The paper presents a method to consolidate context gains into parametric model weights using prospective queries and low-rank feed-forward updates.

The paper evaluates failure modes and confidence calibration across frontier and lightweight language models during entity extraction from cyber threat intelligence reports.

The paper analyzes the performance of retrieval-augmented generation architectures on open-domain question answering tasks involving culturally grounded South Asian narratives.

The paper presents a recursive self-improvement framework using scientific agent swarms that mutate hypotheses and recombine code based on experimental feedback.

Proposes a severity-weighted character error rate that charges text transcription errors based on their practical impact on reader understanding.

Combines privileged teacher signals with value estimation in policy optimization to provide denser token-level supervision for reasoning tasks.

Introduces a benchmark based on real online discussions to evaluate both cognitive and behavioral persuasion skills in language models.

Shows that periodically clearing agent search history and restarting from the top candidate prevents automated research agents from plateauing.

Introduces a self-supervised consistency loss across varied tool interfaces to make language model agents robust against notation and naming changes.

This paper introduces specification templates to define operational boundaries, authority, and oversight for autonomous software agents.

The paper models payment authorization hold lifecycles using formal state machines to generate conformance tests for provider application interfaces.

The authors present a framework that uses localized Taylor approximations to repair high floating-point calculation errors in numerical C routines.

The authors provide a formal correctness contract to identify genuine breaks in asynchronous streaming financial reconciliation systems.

The paper combines formal dynamic logic models and runtime monitoring to verify trajectory-following control in the Autoware autonomous driving platform.

The authors present a software architecture that separates incremental statistical calculations from execution runtimes and data delivery pipelines.

The authors analyze statistical literature to turn visual judgment guidelines into testable correctness specifications for probabilistic programs.

The paper presents a method using domain constraints to tune Kubernetes workload configurations without disrupting live production performance.

The paper compares three cross-project bug localization model architectures across 13 open-source Python projects against limited within-project baselines.

This paper reviews challenges in mobile app analysis across code artifacts, runtime systems, and application ecosystems.

The paper develops quality functions based on distance metrics to assess the diversity of generated test scenarios for automated driving verification.

The paper translates Liquid Haskell into Rocq to provide formal semantics and verify refinement types using an interactive theorem prover.

Formalizes programming language denotational semantics directly in the Agda proof assistant without heavy reformulation.

Mechanizes contravariant universe subtyping in Martin-Löf type theory using the Rocq proof assistant.

Introduces a scenario-based proof methodology to simplify the formal verification of distributed protocols.

Studies the compositional information geometry and Riemannian metrics of probabilistic programs.

Proposes an OpenMP and compiler pipeline extension to offload quantum circuits in high-performance computing.