Vision-OPD introduces regional-to-global self-distillation to improve fine-grained visual understanding in MLLMs. The framework transfers the model's privileged perception on evidence-centered crops to its full-image policy via KL divergence minimization between token distributions. Competitive results on fine-grained visual understanding benchmarks without external models or ground-truth labels.
Multilingual OCR-aware fine-tuning framework for MLLMs combining synthetic OCR-to-translation data generation, LoRA-based SFT, and structured visual chain-of-thought reasoning. Significantly improves extraction of small, blurred, occluded text on receipts, menus, documents under degraded visual conditions. Outperforms GPT-5 and Gemini on OCR grounding and hallucination reduction.
Learning lifted STRIPS+ action models from partial traces with minimal observability assumptions. Authors relax prior work by allowing partial observability of both actions and states. Three cases formalized: no state observability, full observability of selected predicates, local observability of predicates. Completeness results and experiments provided.
QCEA reformulates medical entity alignment as a query-conditioned correspondence problem, integrating semantic encoding and graph-based representation learning. Evaluated on TCM-WM knowledge graphs (SymMap), the model improves Hit@K and MRR metrics, and demonstrates gains in RAG for evidence retrieval and answer accuracy.
Prompt2Fingerprint introduces a framework for LLM fingerprinting via parameter generation. Instead of fine-tuning each model separately, a specialized generator maps textual descriptions to low-rank parameter increments in a single forward pass, eliminating retraining costs.
Automated LLM-based pipeline to generate and tag knowledge components (KCs) for open-ended programming problems. KCGen-KT framework leverages LLM-generated KCs for knowledge tracing. Evaluation on two real-world student code submission datasets shows it outperforms existing KT methods and human-written KCs on future response prediction.
VISAFF is a framework for Emotion Recognition in Conversation (ERC) using vision-language models. It combines two stages: speaker-centered affective grounding and reliability-guided affective complementation. The tuning-free approach leverages frozen VLMs' reasoning capabilities, integrating visual, textual, and acoustic signals to improve accuracy without expensive fine-tuning.
GCE-MIL improves multiple instance learning for whole-slide image analysis by directly optimizing evidence quality (sufficiency, necessity, recoverability) instead of relying on attention weights. Across 81 configurations (9 backbones, 9 datasets), Macro-F1 gains +0.024 and C-index +0.014, with 5× faster inference.
Noise2Noise denoising pipeline for high-throughput Raman spectroscopy using 1D convolutional autoencoder. Trained on repeated short acquisitions (5 ms), no external reference required. Evaluated on mineral sample: RMSE, SNR, SSIM and K-means classification. Preserves chemical coherence while accelerating acquisition.
DiagEval is a trajectory-conditioned diagnostic evaluation protocol for GUI agents testing LLM-generated interactive software. It reuses failed trajectories to determine whether failures stem from the evaluator or the software itself. On WebDevJudge-Unit and RealDevBench, DiagEval recovers 45.6-62.1% of false negatives and improves accuracy from 69.9% to 78.3% and from 65.0% to 81.6%.
CooT is a multi-agent framework using in-context learning for real-time adaptation to unfamiliar partners. Evaluated on Overcooked and Google Research Football, it outperforms population-based methods, gradient-based fine-tuning, and Meta-RL baselines without parameter updates.
pArticleMap combines article embeddings, similarity-graph analysis, and audited LLM workflows to generate evidence-grounded research hypotheses in nanomedicine. The system targets low-density bridge regions and cluster interfaces for discovery support. Retrospective evaluation: 10.8% gold recovery rate, recall@10 of 15.9%, future-neighborhood rate of 61.0%.
Novel approach to emotion modeling using fine-tuned generative language models to output continuous intensity scores (0-100) instead of discrete classification. Demonstrates improved generalization and transfer to sentiment and arousal, particularly valuable for finance applications.
arXiv paper introducing Progressive Generalization Augmentation (PGA) to improve robustness of agricultural RL systems. Coupled RND-PPO architecture + hierarchical noise injection. Results: +8.43% yield, +16.42% nitrogen use efficiency vs BERT-DQN in Florida; 94.4% performance retention under combined perturbations.
Landslide early warning system robust to rainfall forecast uncertainties. Uses Rainfall-Motion-Aware Contrastive Learning (RMCL) to learn representations stable under spatial displacement of rainfall fields. Tested on 2 years of data across Japan (19 regions): 37% higher precision than state-of-the-art baselines.
Position paper arguing that AI model evaluations should be grounded in an explicit theory of capability rather than treating scores as direct measurements. Authors empirically demonstrate that reported performance depends strongly on evaluator modeling assumptions and propose an 'Evaluation Card' to document underlying decisions.
FLAG is a latent diffusion framework for predicting spatial gene expression from H&E images. It integrates a spatial graph encoder and Gene Foundation Model alignment to address the Gene Dimension Curse and preserve biological relationships (gene coordination, spatial distribution). Introduces novel structural evaluation metrics: GSC and SSC.
ChildAgentEval, an interactive benchmark inspired by the WISC scale, evaluates cognitive age alignment of multimodal AI agents on reasoning tasks matched to developmental stages. Results show current agents fail at simple tasks children solve easily, exposing a fundamental gap between AI and human intelligence.
CoLLM-NAS combines two complementary LLMs for neural architecture search: a Navigator LLM guides search direction, a Generator LLM synthesizes candidates. On ImageNet and NAS-Bench-201, the method reduces search costs by 4–10× while outperforming existing NAS methods.
ProtoSiTex is a semi-interpretable framework for fine-grained multi-label text classification. It combines unsupervised prototype discovery with supervised classification using hierarchical loss. Experiments on a new hotel reviews benchmark and two public benchmarks demonstrate SOTA performance with faithful, human-aligned explanations.
New arXiv paper proposing HRC (Hybrid Reward-Cyclic), a reward model explicitly decomposing human preferences into transitive (scalar) and cyclic (vector) components via game theory. Introduces DSPPO (Dynamic Self-Play Preference Optimization) for alignment. Results: +1.23% on RewardBench 2 vs GPM, 44.75% win-rate on AlpacaEval 2.0 with Gemma-2B-it.
Novel adversarial imitation learning algorithm combining off-policy learning with double Q-network stabilization. Reduces sample inefficiency of GAIL by eliminating on-policy algorithm dependency (TRPO) and reward engineering requirements.
Comparative study of LLM agents predicting responses to the SHARE survey (health, ageing, retirement in Europe). Agents based solely on demographic data (age, income, education, etc.) exhibit central tendency bias and unrealistic accuracy, while survey-anchored agents better reproduce interactions between retirement planning factors.
Multi-Scale Hypergraph Laplacians (MSHL): two-stage framework for imputing incomplete spatiotemporal sensor network data. Discovers higher-order structure via multi-scale hypergraphs, then refines with hypergraph-conditioned residual network. Theoretical guarantees and evaluation on real traffic networks with structured outages.
Position paper on interactive evaluation of LLMs. Models deployed as systems acting over time (tools, environments, agents) require evaluation paradigm distinct from static benchmarks. Authors propose taxonomy, design principles, and reporting standards to assess process, recoverability, coordination, robustness, and system-level performance.
CMI (Causal Memory Intervention) selects relevant memories for long-horizon LLM agents through controlled causal interventions rather than semantic similarity. Causal-LoCoMo benchmark introduced with useful memories, distractors, and synthetic harmful memories. CMI outperforms baselines (vector, graph, reflection, summary) in robustness against misleading memories.
FormuLLA fine-tunes LLMs (Llama2, GPT, Claude) on 1400+ FDM formulations to recommend pharmaceutical excipients and predict filament mechanical properties. Llama2 outperforms; smaller models suffer catastrophic forgetting even with this dataset size.
arXiv paper on homogenization in LLMs: models reproduce and amplify human biases through mode collapse. Authors propose a framework to characterize homogenization in terms of normativity (queer theory) and introduce 'xeno-reproduction' as a mitigation strategy promoting diversity. Experiment on Claude 3.5 Haiku demonstrates gender bias.
AutoRubric-T2I automatically synthesizes explicit rubrics to guide Vision-Language Model judges for text-to-image alignment evaluation. Using <0.01% of annotation data required by traditional reward models, it outperforms baselines on MMRB2 and improves generation quality with Flow-GRPO on diffusion models.
MetaCogAgent is a multi-agent LLM framework where each agent evaluates task-capability alignment via a Metacognitive Self-Assessment Unit before execution. The system combines verbalized uncertainty and historical capability profiles to route tasks to best-suited agents. On MetaCog-Eval benchmark (700 tasks), it achieves 82.4% accuracy (+8.7% vs baselines) with 5% fewer API calls than AutoGen.
Attention Hijacking: an adversarial attack that manipulates internal attention distributions in vision-language models to maintain a target response across diverse text queries. The method amplifies visual token influence on response tokens while suppressing textual token competition, improving cross-query transferability.
Multimodal framework for detecting fake news in Indian media combining images and text. Uses ResNet-50 for visual features, DistilBERT for textual embeddings, and an Adaptive Neuro-Fuzzy Inference System (ANFIS) to generate fuzzy reliability scores. Evaluated on IFND dataset with superior results across accuracy, precision, recall, and F1-scores.
AnchorDiff introduces a masked diffusion framework for radiology report generation, integrating clinical anchors derived from knowledge graphs. Unlike traditional autoregressive models, this bidirectional approach uses topology-aware training based on RadGraph and iterative refinement. SOTA on MIMIC-CXR and MIMIC-RG4 benchmarks.
Investigation of extrinsic gender bias in Bangla pretrained language models. Four manually annotated task-specific benchmark datasets constructed (sentiment analysis, toxicity detection, hate speech, sarcasm detection) with minimal-pair gender perturbations. RandSymKL debiasing strategy proposed, combining symmetric KL divergence and cross-entropy loss. Implementation and datasets publicly released.
buddyMe, open-source multi-model framework, integrates three agent interaction paradigms: multi-agent orchestration (Generator-Evaluator), ReAct loops, memory-augmented interaction. Five-stage pipeline tested on 4 real cases (museum guides, weather, tour planning). Results: 20% requirement omission detection, 30% redundant tool invocations, adversarial consensus in 2-3 rounds (70% scenarios).
arXiv paper argues agentic AI cannot be responsible without explicit traceable provenance. Authors formalize provenance through causal attribution function and responsibility tensor, demonstrate it is computable and intervenable online, and identify responsibility gaps in current multi-agent systems.
Alice is an online executable world-model learning system that discovers environment dynamics without rule descriptions or reward signals. The agent induces transition laws from interaction alone, treating preservation conflicts as structural signal to refine hypothesis classes. Evaluation on Baba in Wonderland shows substantial improvement under prior misalignment.
arXiv paper introducing CBEA+LCV, a method for validating commitments in personalized language systems. Rather than treating personalization as recall, the approach structures constraints before generation. Across 360 fixtures, achieves zero failures at 0.49-0.60 availability versus 0.003-0.092 for baselines, with 74-75% payload reduction.
Supervised sparse auto-encoders improve model interpretability by aligning learned features with human semantics. Tested on Stable Diffusion 3.5, they enable compositional generalization and image editing through feature-level intervention.
Adversarial defense for SAR image classification using semantic smoothing. Replaces isotropic noise with structured geometric transformations generated by novel view synthesis, conditioned on acquisition geometry. Improves robustness against FGSM, PGD, OTSA, SMGAA while increasing clean classification accuracy.