CiteVQA requires models to cite specific document regions with bounding boxes alongside answers and finds that even the strongest MLLMs frequently cite the wrong region, with top SAA scores of only 76.0 for closed models and 22.5 for open-source ones.
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What disease does this patient have? A large-scale open domain question answering dataset from medical exams
34 Pith papers cite this work. Polarity classification is still indexing.
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AR-OPD disentangles privileged supervision via anchored residual guidance to reduce hindsight leakage in on-policy distillation, reporting gains of 2.3 points over full privileged OPD and 7.9 over SFT on reasoning tasks.
Defines cost-aware RAG with evidence cost tiers and shows static selectors are brittle while agentic LLM-based selection is promising but model-dependent.
CardioLens is a leakage-resistant CMR testbed of 473k slices and 13k QA pairs showing current MLLMs exhibit a large clinical reality gap with category-collapse failures on real workflows.
Checkup2Action is a new multimodal dataset and benchmark for generating safe, prioritized action cards from real-world clinical check-up reports using large language models.
A graphlet-anchored framework generates 119,856 factually grounded biomedical QA pairs that improve accuracy on PubMedQA and MedQA benchmarks.
SafeAnchor preserves 93.2% of original safety alignment across sequential domain adaptations by anchoring low-rank safety subspaces and constraining orthogonal updates, while matching unconstrained fine-tuning performance within 1.5 points.
Pico reduces LoRA merge interference by calibrating over-shared directions in the B matrix before merging, yielding 3.4-8.3 point accuracy gains and sometimes beating joint training.
PRIMETIME generator reveals that LLM datetime parsing and arithmetic primitives are individually unreliable but fully learnable via fine-tuning, enabling frontier-level accuracy on event planning with small LoRA models.
EduArt is a new benchmark of 871 educational questions that reveals multimodal LLMs perform near ceiling on multiple-choice art history items but drop sharply on open completion and error identification tasks.
HEAL restores FP32-level output reproducibility in 16-bit LLM inference using targeted INT16 quantization and algebraic compensation, cutting overhead by up to 7.1x versus full FP32 on the new MCR-Bench.
BioHarness improves pooled biomedical QA score from 65.9 to 71.0 on 19,302 items by using staged, substrate-aware evidence assembly that escalates only when needed.
Creates the first bilingual clinical benchmark from Brazilian cases and reports that English performance advantage exists only in diagnosis retrieval, disappearing in the other three tasks.
Empirical evaluation across 25 LLMs shows contamination detection methods achieve correct outcomes in only 201 of 335 cases, exposing failure modes from distribution shift and benchmark scale.
MedHarm benchmark shows aligned LLMs and guardrails can still produce unsafe responses on high-risk medical queries, indicating medical safety requires domain-specific testing.
DISeL augments standard LoRA with per-input gates over rank-one updates to reduce catastrophic forgetting during fine-tuning while adding few parameters.
OEP poisons self-evolving LLM agents by constructing clean edge-case experiences that appear locally valid yet cause harmful over-generalization during reflection, achieving over 50% attack success rate on GPT-4o agents across three domains.
CHI-Bench shows current AI agents achieve at most 28% success on long-horizon healthcare workflows that require dense policy adherence, multi-role handoffs, and multi-turn interactions.
Frontier LLMs exhibit premature closure by selecting answers at high rates on medical tasks where the correct choice was removed and on open-ended queries, with safety prompting reducing but not eliminating the behavior.
DKPS-based methods predict new model benchmark scores using cached responses, matching baseline mean absolute error with substantially fewer queries and an offline query selection approach.
MedExAgent models clinical diagnosis as a POMDP with patient and exam noise, then uses supervised fine-tuning followed by DAPO optimization to train an agent that matches larger models on diagnostic accuracy while controlling exam costs.
Counterfactual prompting effects on LLMs are often indistinguishable from those caused by meaning-preserving paraphrases, causing most previously reported demographic sensitivities to disappear under proper statistical comparison.
Exclusive Unlearning makes LLMs safe by forgetting all but retained domain knowledge, protecting against jailbreaks while preserving useful responses in areas like medicine and math.
MOSAIC is a training-free multi-agent LLM framework with rationale, coding, reflection, and debugging agents plus a consolidated context window that outperforms prior methods on scientific coding benchmarks.
citing papers explorer
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CiteVQA: Benchmarking Evidence Attribution for Trustworthy Document Intelligence
CiteVQA requires models to cite specific document regions with bounding boxes alongside answers and finds that even the strongest MLLMs frequently cite the wrong region, with top SAA scores of only 76.0 for closed models and 22.5 for open-source ones.
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When Knowledge Is Not Free: Cost-Aware Evidence Selection in Retrieval-Augmented Generation
Defines cost-aware RAG with evidence cost tiers and shows static selectors are brittle while agentic LLM-based selection is promising but model-dependent.
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Checkup2Action: A Multimodal Clinical Check-up Report Dataset for Patient-Oriented Action Card Generation
Checkup2Action is a new multimodal dataset and benchmark for generating safe, prioritized action cards from real-world clinical check-up reports using large language models.
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BioGraphletQA: Knowledge-Anchored Generation of Complex QA Datasets
A graphlet-anchored framework generates 119,856 factually grounded biomedical QA pairs that improve accuracy on PubMedQA and MedQA benchmarks.
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Crowded in B-Space: Calibrating Shared Directions for LoRA Merging
Pico reduces LoRA merge interference by calibrating over-shared directions in the B matrix before merging, yielding 3.4-8.3 point accuracy gains and sometimes beating joint training.
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EduArt: An educational-level benchmark for evaluating art history knowledge in large language models
EduArt is a new benchmark of 871 educational questions that reveals multimodal LLMs perform near ceiling on multiple-choice art history items but drop sharply on open completion and error identification tasks.
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Beyond English benchmarks: clinical llm evaluation in Brazilian Portuguese
Creates the first bilingual clinical benchmark from Brazilian cases and reports that English performance advantage exists only in diagnosis retrieval, disappearing in the other three tasks.
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CHI-Bench: Can AI Agents Automate End-to-End, Long-Horizon, Policy-Rich Healthcare Workflows?
CHI-Bench shows current AI agents achieve at most 28% success on long-horizon healthcare workflows that require dense policy adherence, multi-role handoffs, and multi-turn interactions.
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Quantifying and Mitigating Premature Closure in Frontier LLMs
Frontier LLMs exhibit premature closure by selecting answers at high rates on medical tasks where the correct choice was removed and on open-ended queries, with safety prompting reducing but not eliminating the behavior.
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MedExAgent: Training LLM Agents to Ask, Examine, and Diagnose in Noisy Clinical Environments
MedExAgent models clinical diagnosis as a POMDP with patient and exam noise, then uses supervised fine-tuning followed by DAPO optimization to train an agent that matches larger models on diagnostic accuracy while controlling exam costs.
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Compared to What? Baselines and Metrics for Counterfactual Prompting
Counterfactual prompting effects on LLMs are often indistinguishable from those caused by meaning-preserving paraphrases, causing most previously reported demographic sensitivities to disappear under proper statistical comparison.
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Exclusive Unlearning
Exclusive Unlearning makes LLMs safe by forgetting all but retained domain knowledge, protecting against jailbreaks while preserving useful responses in areas like medicine and math.
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MOSAIC: Multi-agent Orchestration for Task-Intelligent Scientific Coding
MOSAIC is a training-free multi-agent LLM framework with rationale, coding, reflection, and debugging agents plus a consolidated context window that outperforms prior methods on scientific coding benchmarks.
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Argumentative Large Language Models for Explainable and Contestable Claim Verification
ArgLLMs build argumentation frameworks from LLMs to support explainable and contestable formal reasoning for claim verification.
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Clinically Structured Rank-Gated LoRA for Cross-Benchmark Medical Question Answering
BiRG-LoRA achieves 69.31% macro-average accuracy across CMB, CMExam, MedQA, and MedMCQA, outperforming MoELoRA by 0.89 points with 28.1% fewer trainable parameters under a matched Qwen3-8B protocol.
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TriageRA-CCF: Source-Side Clinical Confidence and Coverage Signals for Adaptive Rank Budgeting in Medical LLMs
TriageRA-CCF combines source-side confidence, coverage, and counterfactual signals to supervise an adaptive LoRA rank router, reporting modest average accuracy gains over LoRA/DoRA/MoELoRA baselines on two 8B models under matched training.
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MedFabric and EtHER: A Data-Centric Framework for Word-Level Fabrication Generation and Detection in Medical LLMs
MedFabric dataset and EtHER detector achieve over 15% better word-level fabrication detection in medical LLMs than prior methods by generating stylistically faithful errors and using decomposition-based checking.
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HeteroRAG: A Heterogeneous Retrieval-Augmented Generation Framework for Medical Vision Language Tasks
HeteroRAG integrates modality-specific retrieval from medical reports and multi-corpus text sources with preference tuning to improve factual accuracy in Med-LVLMs across 11 datasets.
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Medical Incident Causal Factors and Preventive Measures Generation Using Tag-based Example Selection in Few-shot Learning
Tag-based few-shot selection yields higher precision and stability than random or similarity-based methods when using LLMs to analyze medical incidents.
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A Systematic Study of Retrieval Pipeline Design for Retrieval-Augmented Medical Question Answering
Dense retrieval plus query reformulation and reranking reaches 60.49% accuracy on MedQA USMLE, outperforming other setups while domain-specialized models make better use of the retrieved evidence.