CommonWhy is a new dataset of 15,000 why-questions for evaluating LLMs on entity-based causal commonsense reasoning grounded in Wikidata.
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CommitSuite is a large benchmark for commit classification and message generation that includes AST-level changes and LLM annotations, together with a reference-free evaluation framework achieving 0.849 Cohen's Kappa with humans.
CRAFT is a supervised LLM framework using retrieval-augmented generation, self-refinement, fine-tuning, and preference optimization to create fluent adversarial content that boosts target ranks in neural ranking models, outperforming baselines on MS MARCO and TREC benchmarks with cross-architecture
FES-RAG reframes multimodal RAG as fragment-level selection using Fragment Information Gain to outperform document-level methods with up to 27% relative CIDEr gains on M2RAG while shortening context.
EmoTrans is a new video benchmark with four progressive tasks that measures how well current multimodal LLMs handle dynamic emotion transitions rather than static recognition.
Unfine-tuned MLLMs outperform fine-tuned models on remote sensing image captioning when captions are scored by their ability to reconstruct the source image, and a training-free self-correction method achieves SOTA performance.
S-GRPO unifies SFT and RL for LVLMs via conditional ground-truth injection that supplies a maximal-reward anchor when group exploration fails completely.
IAD-Unify unifies industrial anomaly segmentation, region-grounded language understanding, and mask-guided generation in one framework using DINOv2 token injection into Qwen3.5, supported by the new Anomaly-56K dataset of 59,916 images.
PinpointQA is the first benchmark dataset for small object-centric spatial understanding in indoor videos, with four progressive tasks built from ScanNet data.
SWD-Bench evaluates repo-level docs through functionality detection, localization, and completion QA tasks on 4170 entries from PRs, showing best docs raise SWE-Agent issue-solving rate by 20%.
ProAgent uses on-demand tiered perception and context-aware LLM reasoning to deliver proactive assistance on AR glasses, achieving up to 27.7% higher prediction accuracy and 20.5% lower false detections than baselines.
Introduces contextualized code pretraining with caller-callee pairs from static analysis to train CallerGen models that outperform baselines on the new CallerEval benchmark.
DiffCap-Bench supplies a diverse IDC benchmark with ten categories and LLM judging grounded in human difference lists to evaluate MLLMs more robustly than prior lexical metrics.
ClarifySTL uses LLM agents to interactively detect and resolve vagueness and ambiguity in natural language requirements via clarification queries before generating STL formulas, with evaluations on existing and new benchmarks showing effectiveness.
VB-Score shows three major LLMs have severe failures in medical entity recognition and factual consistency, with 13.8% lower performance on chronic conditions affecting older and minority groups, indicating condition-based algorithmic discrimination.
Marketplace Evaluation uses repeated-interaction simulations to assess information access systems with marketplace-level metrics such as retention and market share that complement traditional accuracy measures.
MMP-Refer augments LLMs with multimodal retrieval paths and a trainable collaborative adapter to produce more accurate and explainable recommendations.
Ex2Bundle synthesizes package queries from example bundles using aggregate constraints and applies data-aware relaxation when constraints are infeasible, shown on focused text snippet extraction.
A multi-agent LLM-based framework extracts knowledge graphs from 50 real Ethernet switch manuals with 0.97-0.99 correctness to enable downstream test case specification generation.
DPPMG learns discrete modal-specific preferences via a dedicated GNN from multimodal user data, quantizes them into tokens, and feeds them into generators with a consistency reward to produce personalized text and images.
CodaRAG improves RAG by using a CLS-inspired three-stage pipeline of knowledge consolidation, multi-dimensional associative navigation, and interference elimination, delivering 7-11% gains on GraphRAG-Bench for factual and reasoning tasks.
The UPDP pipeline filters privacy terms and generates de-identified radiology images that preserve diagnostic pathology information, enabling models with competitive disease detection accuracy but reduced identity leakage and improved cross-hospital performance.
ICL4Decomp applies in-context learning to guide LLMs in generating re-executable decompiled code from binaries, reporting roughly 40% higher re-executability than prior methods across datasets and optimization levels.
LLM-based SE tools lack stable ground truth and deterministic outputs, making standard evaluation assumptions invalid and requiring new approaches for reliable assessment.