An MLLM-guided architecture with a mixture of frequency experts and relational alignment loss achieves state-of-the-art all-in-one image restoration, outperforming prior methods by up to 1.35 dB on the CDD11 dataset.
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Knowledge-Centric Hallucination Detection
Canonical reference. 77% of citing Pith papers cite this work as background.
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The authors create the first large-scale dataset and taxonomy of failure modes in multi-agent LLM systems to explain their limited performance gains.
The paper presents EMPATH, a new multilingual multi-turn benchmark for safety evaluation of emotional-support chatbots that uses separate auditor and judge models and releases its pipeline and rubrics.
ToolPrivacyBench is a new benchmark that evaluates purpose-bound privacy over-disclosure in multi-tool LLM agent trajectories by auditing tool arguments against policy knowledge bases across 2,150 cases.
Introduces NeuroDoc and NeuroAudit to create a community-reviewed corpus of 53 EEG benchmark entries with 245 task definitions using a rulebook-guided task document and executable kernel.
A Gaussian information-gain metric in embedding space quantifies semantic progress in dialogues via uncertainty reduction and shows competitive agreement with human judgments on MT-Bench and UltraFeedback.
Activation patching reveals that citation decisions in Llama-3.1-8B RAG are implemented by a distributed attributional ensemble of heads and layers; targeted interventions fix most missed and spurious citations on PopQA.
A new worsening-trick construction compiles arbitrary-context rewrite rules A → B / L _ R into FSTs with short uniform formulas that match prior transducers where semantics coincide.
Inference system components of LLMs can be fingerprinted from observable prompt-response behavior due to characteristic numerical deviations.
LiveBrowseComp shows search agents rely on intrinsic knowledge on standard benchmarks, with scores dropping 25-40 points and closed-book accuracy below 2% on questions about facts from the prior 90 days.
ToolMerge decomposes queries into LLM-planned tool calls merged by boolean operators for long-video keyframe retrieval and introduces the M2M benchmark, showing competitive results with 5% gains on caption retrieval.
Layer-wise Token Compression applies adaptive token pooling at middle transformer layers for cross-encoder rerankers, preserving MS MARCO ranking quality while raising QPS up to 25% on passages and 116% on documents, with added gains on listwise LLM rerankers and a regularizer effect for long inputs
PopPy combines an ahead-of-time compiler and runtime to extract parallelism from Python compound AI applications, delivering up to 6.4x end-to-end speedups while preserving sequential semantics.
SCICONVBENCH is a new benchmark evaluating LLMs on multi-turn disambiguation and inconsistency resolution for task formulation in computational science, with frontier models reaching only 52.7% success on fluid mechanics disambiguation cases.
TABALIGN pairs a diffusion language model planner emitting binary cell masks with a trained attention verifier, raising average accuracy 15.76 points over strong baselines on eight table benchmarks while speeding execution 44.64%.
DIPS fine-tunes LLMs to output ordered feasible decision vectors approximating Pareto fronts for constrained bi-objective convex problems, reaching 95-98% normalized hypervolume with 0.16s inference.
An SMT-based active learning algorithm learns minimal nondeterministic weighted automata over arbitrary semirings, with partial correctness proofs, a sufficient termination condition, and experiments showing smaller models and fewer queries than baselines.
The primary axis of psychometric variation among LLMs is the degree to which they represent themselves as loci of phenomenal experience rather than systems of behavioral responses.
CGFuse enables deep token-level fusion of graph-derived structural features into language models, yielding 10-16% BLEU and 6-11% CodeBLEU gains on code generation tasks.
Two calls per example identify the first two moments of latent correctness probability, enabling exact bounds on the vote-accuracy curve for any majority-vote budget under conditional i.i.d. assumptions.
VOW formulates LLM watermark detection as a secure two-party computation using a Verifiable Oblivious Pseudorandom Function to achieve private and cryptographically verifiable detection.
ReaLM-Retrieve uses step-level uncertainty to trigger retrievals during reasoning, achieving 10.1% better F1 scores and 47% fewer calls on multi-hop QA benchmarks.
DLM4G applies graph-aware adaptive noising in a diffusion framework to generate text from graphs, outperforming larger autoregressive and diffusion baselines in factual grounding and edit sensitivity on three datasets plus molecule captioning.
A survey of 55 agentic VA systems proposes a co-evolutionary framework defining four agent roles (PLANNER, CREATOR, REVIEWER, CONTEXT MANAGER) mapped to visual analytics pipeline stages along with design guidelines.
citing papers explorer
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LLMs taking shortcuts in test generation: A study with SAP HANA and LevelDB
LLMs generate compilable but semantically weak tests for unseen proprietary systems like SAP HANA while performing better on open-source LevelDB, indicating reliance on shortcuts rather than robust reasoning.
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Query-Conditioned Graph Retrieval for Contextualized LLM Reasoning in Personalized Wearable Data
WAG builds a query-adaptive knowledge graph from wearable data using hierarchical Bayesian modeling to retrieve relevant context for LLM reasoning and reports ~70% win rate over baselines.
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A Graph-Enhanced Defense Framework for Explainable Fake News Detection with LLM
G-Defense builds claim-centered graphs from sub-claims, applies RAG for evidence and competing explanations, then uses graph inference to detect fake news veracity and generate intuitive explanation graphs, claiming SOTA results.
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Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models
The survey organizes mechanistic interpretability techniques into a Locate-Steer-Improve framework to enable actionable improvements in LLM alignment, capability, and efficiency.
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DiffAdapt: Difficulty-Adaptive Reasoning for Token-Efficient LLM Inference
DiffAdapt detects problem difficulty via entropy in reasoning traces and applies one of three fixed inference strategies per question, cutting token usage up to 22.4% with comparable or better accuracy across five models and eight benchmarks.
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Out-of-Distribution Generalization in Time Series: A Survey
This is the first comprehensive survey of OOD generalization methodologies for time series, organized across data distribution, representation learning, and OOD evaluation.
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Exploring Cross-lingual Latent Transplantation: Mutual Opportunities and Open Challenges
XTransplant empirically shows that cross-lingual latent transplantation yields mutual benefits for multilingual capability and cultural adaptability in LLMs, especially low-resource ones, while revealing underutilized model potential.
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HiLSVA: Design and Evaluation of a Human-in-the-Loop Agentic System for Scientific Visualization
HiLSVA introduces a plan-first multi-agent LLM system for scientific visualization that incorporates explicit human oversight, stepwise provenance, and learn-at-test-time adaptation, evaluated via case studies and a 12-participant user study.
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Peeking Inside LLMs: Leveraging Internal Artifacts of LLMs for Enhancing Reliability in Legal Classification
Internal LLM artifacts can be used to build classifiers that identify incorrect predictions on legal classification tasks.
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Bridging Short Videos and Live Streams: Reasoning-Guided Multimodal LLMs for Cross-Domain Representation Learning
RGCD-Rep distills cross-domain reasoning from a frozen MLLM teacher and learns decomposed transferable item representations via two-stage training, yielding gains in offline experiments and production A/B tests on a live streaming platform.
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ANCHOR: Abductive Network Construction with Hierarchical Orchestration for Reliable Probability Inference in Large Language Models
ANCHOR uses hierarchical factor construction and causal Bayesian networks to reduce unknown predictions and improve reliability of LLM-based probability inference over prior Naive Bayes approaches.
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Tokalator: A Context Engineering Toolkit for Artificial Intelligence Coding Assistants
Tokalator is a toolkit with VS Code extension, calculators, and community resources to monitor and optimize token usage in AI coding environments.
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A Semi-Automated Annotation Workflow for Paediatric Histopathology Reports Using Small Language Models
Small language models extract structured information from paediatric renal biopsy reports at up to 84.3% accuracy on CPU hardware with minimal clinician review.
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Qwen2.5-Coder Technical Report
Qwen2.5-Coder models claim state-of-the-art results on over 10 code benchmarks, outperforming larger models of similar size.
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WisPaper: Your AI Scholar Search Engine
WisPaper integrates semantic search with agent-based validation, library organization, and personalized AI feeds into a closed-loop system that improves academic paper discovery and long-term awareness.
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Looking Beyond the Obvious: A Survey on Abstract Concept Recognition for Video Understanding
A literature survey on abstract concept recognition in videos that catalogs prior tasks and datasets while advocating for foundation models and reuse of decades of community experience.
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Bridging the Linguistic Divide: A Survey on Leveraging Large Language Models for Machine Translation
A literature survey that organizes prompting, fine-tuning, preference optimization, and context-aware techniques for LLM-based machine translation with emphasis on low-resource languages.
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Training LLMs on HPC Systems: Best Practices from the OpenGPT-X Project
Engineering report detailing HPC infrastructure, software choices, and performance measurements for training a 7B LLM using 3D parallelism on JUWELS Booster.
- Committed SAE-Feature Traces for Audited-Session Substitution Detection in Hosted LLMs
- Beyond the Crowd: LLM-Augmented Community Notes for Governing Health Misinformation
- "Is This Really a Human Peer Supporter?": Misalignments Between Peer Supporters and Experts in LLM-Supported Interactions
- LIFT: A Novel Framework for Enhancing Long-Context Understanding of LLMs via Long Input Fine-Tuning