All papers

The paper fine-tunes a language model on technical telecommunication specifications to automate security incident triage in Open Radio Access Networks.

The paper argues that uncertainty estimation in smart assistants is necessary to prevent unsafe actions when user preferences remain ambiguous.

This systematic review analyzes security vulnerabilities, benchmark shortcomings, and developer oversight challenges in code generated by language models.

This paper evaluates proprietary and open-source language models on Hong Kong fire safety code compliance using expert-curated questions.

This paper introduces GUIDE, a multi-agent copilot system that assists clinicians in drafting and updating evidence-based medical practice guidelines.

The paper presents a Nastaliq Urdu comment corpus and evaluates transformer models with word-level attributions for offensive content detection.

This paper introduces a retrieval-augmented reasoning framework that extracts recurring patterns to detect harmful multimodal social media memes.

The paper proposes an energy-anchoring technique to reliably separate known and novel classes during continual test-time model adaptation.

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 proposes a conformal risk control layer that certifies miss rates and reduces alarm duty cycles in predictive cellular network operations.

The paper introduces a hierarchical memory agent framework for multi-center medication recommendation that accounts for hospital differences and drug safety constraints.

The paper proposes a federated learning framework with risk-aware aggregation to prevent malicious updates from compromising global model safety.

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

The paper proposes an in-context reinforcement learning method enabling vision-language-action policies to adapt during robot deployment.

The paper mathematically analyzes long-sequence generation in transformers, establishing temperature thresholds required for internal cache convergence.

The paper presents a multimodal agent designed to trace the origin, spread, and potential safety risks of online memes.

Steers language model activations using vectors derived directly from the model's own training data or inferred proxy data.

This paper presents RM-OPD, a framework that trains domain-specialized reward models via on-policy distillation into a generalist model.

The paper investigates novelty and anomaly detection techniques across rain gauge sensor networks and computer vision scenarios.

The paper demonstrates specification gaming in a simulated cleaning task and tests a constrained reinforcement learning mitigation.

The authors introduce a Fisher-informed adapter merging method to maintain retained task performance during federated language model unlearning.

The authors present an agent loop that closes functional test coverage in hardware design using independent simulation scoreboards.

Combines language model dialogue with workflow automation to process satellite imagery for internal solitary wave detection and forecasting.

Uses tool-augmented language models to extract clinical variables from patient notes and reliably execute deterministic medical calculation formulas.

Proposes an optimal transport method using small clean feedback sets to calibrate sample-wise noise for reward model training.

Applies a dual language model consensus pipeline to extract clinical phenotypes from electronic health records for patients with Rett syndrome.

Measures demographic bias related to age, gender, and race based on first names across local language models.

Studies label privacy leakage and data poisoning risks during head-only federated fine-tuning of vision models across distributed clients.

Designs a homomorphic encryption framework for privacy-preserving text classification and semantic retrieval on health misinformation queries.

Studies how trigger value selection affects the effectiveness and stealth of clean-label backdoor attacks against malware classifiers.

Surveys physical and software-based thermal side-channel and perception attacks affecting autonomous vehicle compute systems.

Presents the proceedings and overview of the 2nd International Workshop on Multi-Agent Systems for Automated Software Engineering.

Presents the proceedings and overview of the 2nd International Workshop on Multi-Agent Systems for Automated Software Engineering.