All papers

This paper presents a mid-training method, corpus, and benchmark to help language models understand and reason about time series data.

Presents a hypothesis testing framework using shadow models to audit whether a language model was distilled from a proprietary teacher.

Introduces an evolutionary optimization method using multi-turn reflective mutators to improve production data agents and pipelines.

The paper introduces a benchmark to evaluate how well language models understand legal philosophy and apply it to legal reasoning.

This paper introduces a counterfactual benchmark to evaluate honorific register allocation bias across gender and occupations in English-to-Bengali translation.

Combines genetic programming and diagnostic evaluation loops to discover quantitative financial trading factors without backtest overfitting.

The paper proposes an edge-cloud collaborative inspection framework using multimodal LLMs dynamically scheduled based on scene complexity.

The paper introduces a framework using psychometric dimensions and player personas to benchmark how language models evaluate and refine video games.

This paper introduces an evaluation benchmark using multimodal models to assess generated videos across human preference dimensions.

This paper audits Chinese language models for bias and error propagation across formal and ironic social registers.

The paper introduces an agent-based approach that adapts search spaces to optimize post-training recipes and configurations for pre-trained language models.

The paper presents a double-annotated sentiment dataset for Nepali and evaluates several open-source and commercial language models on it.

The paper proposes an architectural ML Plane to expose and audit privacy risks of federated learning in operational technology intrusion detection systems.

This paper presents a robotic manipulation framework combining high-level language planning with vision-language verification to enforce physical and kinematic constraints.

The paper introduces a decomposed attention mechanism for vision-language models to separate visual and textual token processing.

The authors analyze theoretical sample-complexity tradeoffs between questions and generations when ensembling multiple language models at test time.

The paper proposes a framework that embeds keyed Fourier-phase signals into face images to enable verification against publisher release records.

This paper introduces a multi-agent retrieval framework that coordinates search across legal statutes and precedent cases for grounded legal reasoning.

The paper presents an open-source multimodal language model combining a small language backbone with vision encoders for efficient inference.

The paper introduces a visual and multimodal knowledge graph grounding model designed for visual navigation tasks.

The paper introduces confidential error-correcting codes, providing a theoretical framework and concrete construction balancing message privacy with reliable decoding.

This paper presents an ecosystem of benchmark datasets and evaluation frameworks to advance machine learning applications in veterinary medicine.

This paper proposes a progress-aware credit assignment framework for multi-turn reinforcement learning in language model agents without using critic models.

The paper evaluates representation-based defenses against backdoors in federated intrusion detection systems using disjoint server data.

The paper introduces HomogeneityNLI to evaluate whether open-weight language models recognise truth-value gaps in plural predication.

The paper introduces a framework combining guidelines and agentic linking to ground medical concepts across diverse text types and ontologies.

The paper proposes a modular vision-and-language navigation framework combining dynamic memory pruning and language-model spatial reasoning.

The paper presents a multimodal vision-language model for Alzheimer's disease classification and question answering capable of handling missing imaging or clinical data.

The paper introduces a decision-layer watermarking method for multi-agent routing to protect routing policies against unauthorized cloning.

The paper introduces a large-scale benchmark of synthetic underwater audio records to evaluate audio-language models on acoustic recognition tasks.

The authors propose a memory-driven vision-language navigation system using temporal compression and geometric-semantic representations to support long-horizon trajectory tasks.

This paper introduces a framework combining zero-knowledge proofs and serving certificates to verify model selection and query rewriting in routers.

Surveys methods for compressing large language model benchmarks to reduce evaluation costs while maintaining accurate score estimates.

The authors propose an embodied world model that unifies RGB video, metric depth, and robot actions into a shared dynamic representation.

Introduces a mixture-of-prompts distillation method to transfer knowledge from hard prompts to soft prompts, improving generalization on unseen classes.

This paper evaluates whether existing deepfake detection models can identify physical humanoid face robots performing speech-synchronized lip movements.

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

The paper reviews language-vision fusion architectures and deployment strategies for medical diagnosis in low-resource healthcare settings.

Evaluates whether framing explanations around unreliable narrator theory enables open-weight models to explain satirical news cost-effectively.

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

This paper establishes mathematical conditions to guarantee long-term performance and low counterfactual regret for reflective language agents under resource constraints.

The paper presents a framework for balancing multimodal integration using conditional dependency modeling across diverse data signals.

Compares human and Llama 3.1 8B facilitation turns across design meeting transcripts using multiple computational behavioral metrics.

The authors introduce a parameter-free conformal martingale method modeled as a universal portfolio to quickly detect distribution shifts at unknown times.

The paper analyzes aesthetic judgments by foundation models on K-pop music videos to measure inter-model consensus across audio and visual modalities.

This paper introduces a multimodal meme dataset and a pipeline to extract entities and their semantic relationships.

Presents a multi-agent swing trading framework separating long-term strategy formulation from fast-timescale portfolio risk governance.

This paper presents a framework and a dataset to generate holistic full-body co-speech gestures from audio and masked movement priors.

The paper analyzes the theoretical detection limits and sum-test efficiency of textGrain watermarking under human text edits.

This paper synthesizes literature on educational language models to evaluate whether tutoring systems measurably improve student learning gains.

This paper introduces LoongSage, an asynchronous reinforcement learning framework designed to run multi-turn agent tasks with reduced system overhead.

The paper proposes an inference-time alignment method that optimizes expected costs while controlling tail risk in diffusion models.

Introduces Agentic-MatriBO, a modular Bayesian optimization framework using LLM agents and deterministic compilation for material and chemical experiment design.

Presents a local agentic framework using smaller language models to automatically synthesize edge-case testing scenarios for autonomous systems.

This paper establishes authorization models and integrity proofs to prevent narrative model history from becoming unauthorized actions.

The paper presents an instrumented predator-prey simulation framework to evaluate communication, scheduling, and collective dynamics in remote decision-making agents.

Introduces a benchmark and environment for evaluating coding agents on text-conditioned LEGO design with physical and discrete assembly constraints.

The paper proposes an embedding-based graph kernel entropy method for computationally efficient hallucination detection in language models.

The paper presents a multi-stage automated labeling framework using multimodal models and agents to annotate short videos for content moderation.

Proposes a framework modeling research ideation as modular thinking flows sampled dynamically from a probabilistic supernet.