All papers

The paper introduces a benchmark to measure vision-language-action model robustness against hardware, optical, and physical robot setup variations.

The paper presents a reinforcement learning framework that trains language models to optimize molecules across multiple turns using iterative feedback.

The paper develops a framework explaining why large rollout batches in reinforcement learning reduce sample efficiency despite maintaining drift thresholds.

The paper presents a method for generating visual artifact code by combining text prompts with generated image guidance and cross-modal rewards.

The paper introduces a training-free contrastive decoding method where a small model guides a larger model toward shorter reasoning chains.

The paper studies how demonstration quality weighting, filtering, and conditioning affect vision-language-action model pretraining and downstream performance.

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

This paper compares the quality and coverage of unit test cases generated by ChatGPT against an automated generation tool.

This paper introduces an open-source infrastructure and benchmark for multi-fidelity AI-assisted photonic simulation and inverse design.

Summary pending (next check).

This paper examines challenges and recent machine learning approaches for automating FPGA high-level synthesis hardware design.

Summary pending (next check).

Summary pending (next check).

Summary pending (next check).

Summary pending (next check).

Summary pending (next check).

Summary pending (next check).

Summary pending (next check).

Summary pending (next check).

The paper compares traditional cloud encryption methods with artificial intelligence based encryption techniques across several performance metrics.

Summary pending (next check).

The paper proposes a persistent homology metric to evaluate the topological and structural stability of time-varying social networks.

Summary pending (next check).

Summary pending (next check).

The paper reviews retrieval-augmented generation paradigms and examines frameworks across retrieval, generation, and modular components.

The paper presents an 8-bit weight and activation post-training quantization technique that migrates activation outliers to weights.

The paper introduces a post-training mixed-precision quantization method using GPU-compatible 2D weight tiles for efficient inference.

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

The paper introduces an open-source library to construct validation suites that stress test the reliability of LLM judges.

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

Uses coarse CTC decoding to filter relevant hotwords into LLM prompts for improved contextual speech recognition on rare words.

Compares sparse, dense, hybrid, and fusion RAG architectures across GPT-4o and LLaMA deployments using the RAGAS evaluation protocol.

The paper introduces a zero-shot LLM pipeline to generate counterfactual examples for stress-testing NLP models.

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

Introduces a reinforcement learning method where agents learn ad-hoc collaboration by training against partners with varied convention adaptability.

Synthesizes control policies for finite MDP agent populations under aggregate reach-avoid chance constraints using moment propagation and Cantelli inequalities.

Proposes distributed harmonic-covariance feedback using Hodge Laplacians for hole closure in planar multi-agent robot swarms.

Formulates a robust trajectory and timing planner for coordinated UAV-UGV aerial monitoring teams under energy and precedence constraints.

Proposes an algorithm for multi-robot submodular maximization under communication k-connectivity constraints resilient to robot failures.

Investigates conditions under which communication signals emerge between simulated autonomous robots performing spatial foraging tasks.

Analyzes the theoretical limits of agent manipulation in personalized incentive systems where the regulator learns user profiles over time.

Analyzes computational complexity of limit-sure winning objectives for cooperative agent teams lacking shared randomness in stochastic games.

Develops trajectory generation and geometric control for dual-quadrotor cooperative transport of payloads with sliding cable attachments.

Introduces a configuration-driven platform for reproducible multi-agent reinforcement learning experiments within the ARGoS swarm robotics simulator.

The paper examines how multi-agent Q-learning movement policies generate spatial clustering and traveling bands in spatial public goods games.

The paper models structured sentiment analysis as dependency graph parsing implemented through sequence labeling across five languages.

The paper proposes a four-tensor attention architecture that jointly normalizes attention over semantic and temporal fibers for scene prediction.

The paper defines evaluation metrics to show that language models continue generating reasoning traces even when instructed not to think.

The paper proposes a sparse factor framework to map multimodal behavioral data to interpretable concepts for depression assessment.

The paper demonstrates that entropy-based byte patching misses predictable calculation points and proposes a boundary-dependence signal to fix it.

The paper develops an LLM-based pipeline to automatically detect outcome switching and presentation spin in clinical trial reports.

The paper benchmarks a specialized decision model against general-purpose LLMs on English and Chinese stance detection tasks.

The paper introduces a method to unlearn target knowledge while recovering collateral capability loss in non-target tasks.

The paper describes training a competitive decoder-only search reranker from scratch using modest computational resources and common datasets.

The paper introduces a 7,000-task benchmark to evaluate how multimodal models selectively use or ignore conversational context.

The paper benchmarks a specialized multiple-choice decision model against 19 general-purpose LLMs across knowledge, reasoning, and multilingual tasks.

The paper reviews LLM-based first-order logic autoformalization, distinguishing ontology extraction from logical translation and outlining key benchmarking challenges.

The paper introduces an educational tool combining Arabic knowledge graphs and passage retrieval to generate narrative-based questions.

The paper meta-analyzes 25 years of automated verbal deception detection models and identifies a persistent accuracy ceiling around 70 to 75 percent.

The paper shows that whole-context KV cache rehearsal harms compression under tight budgets, proposing targeted rehearsal to preserve answer-relevant entries.