LMAC leverages LLM reasoning to design communication protocols in MARL, enabling agents to reconstruct the underlying state uniformly and accurately. The approach iteratively refines protocols using an explicit state-awareness criterion. Experiments on MARL benchmarks demonstrate substantial performance gains over prior baselines.
CodeBind introduces a multimodal alignment framework using shared-specific compositional codebooks. The method decomposes representations into semantic shared components and modality-unique components, validated across 9 modalities (text, image, video, audio, depth, thermal, tactile, 3D point cloud, EEG) achieving state-of-the-art performance in classification and retrieval tasks.
Authors identify that trajectory entanglement in Rectified Flow stems from nonzero divergence regions in the learned velocity field. They propose an offline correction that attenuates the divergent component during coupling generation, with no deployment overhead. Improvements validated on 2D benchmarks and image generation.
ESI-Bench is a benchmark for embodied spatial intelligence spanning 10 task categories on OmniGibson. Experiments show active exploration outperforms passive approaches, but models fail primarily from "action blindness": poor action choices lead to poor observations and cascading errors. Models lack metacognition compared to humans.
AdaSwitch proposes a cloud-local collaborative paradigm where a local agent (small LLM) handles simple tasks and requests assistance from a cloud agent (large LLM) for complex reasoning. The adaptive mechanism detects local errors and dynamically switches. Evaluation on 7 benchmarks (mathematical reasoning, complex QA) shows performance improvement with reduced computational overhead.
Research integrating safety filtering and Constitutional AI into federated LLM training (FedLLM). Authors demonstrate these techniques improve safety by over 20% on AdvBench, mitigating risks of unsafe model aggregation and redistribution across clients.
Study of factual inconsistencies in multilingual Wikipedia tables. Researchers developed methodology to collect and analyze tables across 300+ language versions of Wikipedia, identifying inconsistency categories. Implications for fact verification and reliability of AI systems trained on Wikipedia.
Novel approach to extend Knowledge Graphs for French cultural heritage. Authors introduce WJoconde, a multimodal KG integrating text and images, with three variants and a benchmark for Knowledge Graph Completion. They propose a framework combining LLMs and Vision-Language Models for automated data extraction and validation, improving KG reliability.
Novel 1-bit LLM quantization method leveraging pre-trained models. Uses consistent progressive training (forward/backward) with binary-aware initialization and dual-scaling compensation to convert weights to binarized representation. Reduces training costs and accuracy degradation versus existing approaches.
ES-CoT detects answer convergence during chain-of-thought generation to stop inference early. The method reduces inference tokens by 16.08% on average across six reasoning benchmarks while maintaining comparable accuracy to standard CoT.
EnoTab is a dual denoising framework for TableQA addressing complex questions and large-scale tables. It decomposes questions into minimal semantic units and prunes tables via an explicit evidence tree with post-order node rollback mechanism for abnormal states. Achieves strong performance on complex TableQA tasks.
AMBS (Adaptive Multi-Branch Steering) aligns LLMs on three simultaneous objectives (Helpfulness, Harmlessness, Honesty) via a 1-to-N Transformer framework. A shared representation is replicated into N objective-specific pathways with constrained transformations. Results: 56.5% avg WR on LLaMA-2-7B, 189 Tok/s.
NeuSymMS is a hybrid neuro-symbolic memory system for LLM agents. It couples neural fact extraction from dialogue with a CLIPS-based expert system that classifies, deduplicates, and reconciles facts. Knowledge is stored as subject-relation-value triples in a relational database, with short/long-term memory and access-based promotion.
Theoretical study of multi-objective evolutionary algorithms for multi-party optimization (MPMOP). On MP-JCG benchmark, payoff-guided mutation requires Θ(n²) fitness evaluations to cross a gap region, while CPR-NSGA-II achieves O(n log n) via cross-party recombination. Runtime analysis on BPBOMST (multi-party minimum spanning tree) with instance-parameterized bounds.
TTE-Flash replaces explicit Chain-of-Thought traces with latent think tokens to accelerate reasoning-aware multimodal representations. TTE-Flash-2B outperforms explicit-CoT counterparts on MMEB-v2 while maintaining constant inference cost. Latent tokens remain interpretable both textually and visually.
arXiv study evaluating ChatGPT's consistency in coding communication data across demographic groups (gender, race). Authors adapt an automated scoring framework and test ChatGPT on three collaborative task types. Result: ChatGPT coding shows consistency comparable to human raters across groups.
arXiv paper evaluating how language and reasoning models assess board games. Testing 100+ games with 450 human judgments, reasoning models align better with humans than standard LLMs for evaluating game fairness and fun. Paradox: as models approach game-theoretic optimality, their fit to human judgments weakens.
Template Infilling (TI) is a conditioning methodology for Diffusion Language Models that aligns structural anchors across the entire response space, replacing prefix prompting. Evaluated on mathematical reasoning, code generation, and trip planning, TI achieves 9.40% improvements and accelerates multi-token generation.
QQJ is an evaluation framework for generative AI combining expert-designed multi-dimensional rubrics and LLM evaluator calibration on small high-quality annotation sets. Tested on text and image generation, QQJ shows stronger alignment with human judgment than traditional automatic metrics and unconstrained LLM-based evaluators.
New IBAL method to strengthen MARL robustness against inter-agent interaction disruptions. Framework uses information-theoretic approach to construct attacks that degrade coordination by perturbing observations and actions, then trains agents to remain reliable. Demonstrated improvement over existing baselines and agent-missing scenarios.
CardioThink, a physician-inspired MLLM framework, structures ECG diagnosis through explicit reasoning stages (rhythm, conduction, morphology, impression) to enhance interpretability. Structured Set Policy Optimization (SSPO) aligns clinical reasoning without manual annotations, outperforming direct prediction approaches across ECG benchmarks.
LISTEN is an agentic LLM framework for selecting among multiple options with competing objectives. Two iterative algorithms: LISTEN-U refines a parametric utility function, LISTEN-T uses tournament-style selection on small batches. Evaluated on flight booking, shopping, exam scheduling. Code available.
VolTA-3D is a self-supervised 3D Vision Transformer framework for brain MRI. It aligns global and local tokens in a student-teacher paradigm and enforces fine-grained structural reconstruction. Evaluated on hippocampal segmentation and classification tasks (sex, Alzheimer's), it outperforms random baselines and demonstrates improved transferability across domain shifts.
Response-free item difficulty modelling for multiple-choice questions using fine-tuned transformers. End-to-end approach on item wording eliminates manual feature engineering. Multi-task variant with auxiliary QA objective delivers significant improvements in small-sample regimes.
GraphMind combines GNN and LLM for multi-step mathematical reasoning. The framework models reasoning as an evolving heterogeneous graph where nodes (conditions, theorems, conclusions) and edges (logical dependencies) enable dynamic theorem selection and iterative conclusion generation. Improved results on QA benchmarks.
Distinguishable Deletion (D²) unifies knowledge deletion and refusal for LLM unlearning. The method uses an energy index to erase undesirable knowledge in latent representations rather than specific tokens, avoiding biased deletion and re-emergence of harmful content. Energy-based Unlearning Alignment (EUA) applies this mechanism at training and inference.
QE-Catalytic-V2 is a unified graph-text multimodal LLM for catalytic materials. It integrates property prediction and inverse design in a single shared representation space, eliminating distribution shifts between decoupled models. Demonstrates superior performance on relaxed-energy prediction and inverse design tasks.
CAREBench is a benchmark evaluating LLMs' emotion understanding through cognitive appraisal reasoning. Tested on 6 models with complete inferential chain annotations (first/third-person perspectives), it shows stronger models match humans on some tasks but fall short on appraisal reasoning and positive emotion recognition.
Study evaluating 8 multimodal models (Gemini-2.5-Pro, o3, etc.) on robustness against cognitive biases in Chinese short-video misinformation. Manually annotated dataset of 200 videos across 4 health domains. Gemini-2.5-Pro achieves 71.5/100, o3 scores 35.2. Models are susceptible to social cues like authoritative channel IDs.
Annotation framework using multilingual Llama3.1 as teacher model to distill expert knowledge for medical text tagging in Polish. DistilBERT achieves F1 > 0.80 across 5 clinical categories (Radiology, Oncology, Cardiology, Hypertension, Pathology) with 500× fewer parameters and 300× lower GPU VRAM than LLMs.
Shallow neural network agents master the card game Schnapsen through reinforcement learning. RLBot, trained via asynchronous Monte Carlo updates, outperforms MLPBot (supervised imitation) and achieves statistically significant wins against RdeepBot, a search-based baseline. Combining learned value functions with deeper lookahead during gameplay improves performance.
COMPACT, a multi-teacher CoT distillation framework, adaptively fuses supervisions from multiple LLMs into compact student models. It dynamically weights teacher gradients using three metrics: graph-based consensus, mutual-information-based adaptability, and loss-based difficulty. Achieves SOTA results across benchmarks while mitigating catastrophic forgetting.
Analysis of 130,882 images from 368 remix parties on Artbreeder (13 months). Images converged toward common thematic attractors (steampunk, alien architecture) while becoming simpler. Paradox: more novel parents produced more complex, liked children, yet users preferred remixing less novel images.
Automated heuristic discovery via continuous optimization in latent space. Encoder maps discrete programs to continuous embeddings, differentiable surrogate model predicts performance, invertible normalizing flow regularizes optimization trajectory. Evaluation on TSP, CVRP, KSP, and Online Bin Packing shows competitive results against evolutionary baselines.
ECC, a query clustering algorithm, calibrates semantic embeddings through model comparisons to align surface semantics with latent LLM capabilities. Using a Bradley-Terry model, it improves capability ranking by 17.64 points over human-labeled baselines and 18.02 points over embedding-based baselines, with applications to query routing.
New zeroth-order hard-thresholding algorithm with variance reduction for ℓ0-constrained optimization. Addresses SZOHT's limitation on random directions by mitigating conflict between ZO gradient deviation and hard-thresholding expansivity. Improved convergence rates validated on ridge regression and black-box adversarial attacks.
Study of prompt compression on LLaDA, an 8B-parameter DLLM, using LLMLingua-2. Evaluation on GSM8K, DUC2004, ShareGPT at 2× compression ratio shows semantic preservation does not guarantee stability in diffusion models: mathematical reasoning degrades substantially while summarization remains robust. Autoregressive compression methods do not transfer uniformly to DLLMs.
New TDA method for diffusion models using mirrored unlearning and noise-consistent skew. MUCS fine-tunes a second model with bounded mirrored gradient ascent and measures normalized skew using consistent noise samples. Outperforms existing approaches on three datasets.
UniTrans translates heterogeneous sensor modalities in real-time for collaborative perception without retraining. A universal model combines translator experts parameterized via an intrinsic latent space, validated on OPV2V-H and DAIR-V2X with open-source code.
ViTC-UNet conditions a UNet on frozen pre-trained Vision Transformer representations via learnable tokens and two-way attention decoder. The approach improves biomedical semantic segmentation on MRI and CT without end-to-end fine-tuning, combining ViT global priors with UNet local inductive bias and high-resolution decoding.