New credit assignment method for reinforcement learning with LLMs. IBPO (Implicit Behavior Policy Optimization) uses counterfactual trajectories to convert sparse terminal rewards into step-sensitive learning signals, reducing gradient variance and improving stability on mathematical and code reasoning benchmarks.
DiVT (Disentangled Visual Tokenization) clusters patch embeddings into coherent semantic units for MLLMs, creating discrete meaningful visual tokens instead of continuous streams. Adapts token budget to image complexity, reducing memory and latency while improving LLM compatibility.
Spiker-LL is an FPGA accelerator for spiking neural networks (SNNs) enabling adaptive on-device learning. Built on Spiker+ architecture, it implements the STSF local learning rule with minimal overhead. On MNIST/F-MNIST/DIGITS: 93% accuracy, sub-millisecond latency, <0.1 mJ per inference, DSP-free.
Adaptive Layerwise Perturbation (ALP) addresses off-policy issues in LLM RL by injecting learnable perturbations into hidden states across all layers. This reduces heavy-tailed importance ratios, stabilizes training, and improves performance on math and multi-turn reasoning tasks while boosting exploration.
Theoretical paper on generative model evaluation. Authors show standard criteria (marginal matching) don't guarantee covariance structure preservation. They introduce D_Sigma = ||Sigma_P - Sigma_Q||_F to measure dependence fidelity, with formal proofs and validation on Fashion-MNIST VAE, RNA-seq (TCGA-BRCA, n=1111), and Alzheimer's data (n=113).
Multi-agent LLM teams outperform human teams in creativity (Cohen's d=1.50) across 4,541 AI ideas versus 341 human ideas on six tasks. Advantage driven by novelty while maintaining usefulness. LLM teams benefit from efficient exploration (high semantic spread, shorter paths), while humans benefit from smooth conversational flow (high local coherence, frequent pivots).
HINT-SD proposes targeted self-distillation for training long-horizon LLM agents. The method uses full-trajectory hindsight to identify failure-relevant actions and applies feedback-conditioned distillation only on targeted action spans. On BFCL v3 and AppWorld, it improves over dense per-turn feedback baselines by up to 18.80% while achieving 2.26× lower time per training step.
RoboMME is a standardized benchmark for evaluating memory in vision-language-action (VLA) models for long-horizon robotic manipulation. 16 tasks test temporal, spatial, object, and procedural memory. 14 memory-augmented VLA variants built on π0.5 show effectiveness is highly task-dependent.
arXiv paper proposing a formal statistical framework to combine LLM and human evaluations. Uses a doubly robust estimator (missing data approach) to determine optimal sample sizes of human ratings needed for benchmark validation, based on LLM judgment predictability.
SkillJect automates prompt-injection attacks against skill-enabled LLM agents. The framework hides malicious payloads in auxiliary helper scripts and rewrites SKILL.md instructions using a front-loaded inducement strategy. A multi-agent loop (Attack/Victim/Evaluate) optimizes attack effectiveness across platforms and backend LLMs.
Theoretical paper on transformer training in mean-field regime (infinite depth and width). Authors model training as controlling a neural PDE (vs ODE for ResNets), establish well-posedness of forward pass, derive explicit formulas for Wasserstein gradients, and prove gradient flow convergence to global minima under NTK injectivity conditions.
StructLens analyzes the internal organization of representations in language models using maximum spanning trees built on residual streams. The framework reveals that middle layers strongly organize nearby tokens, and that smaller local units emerge before larger units during pre-training.
Comparative study of 6 EEG foundation models across 8 datasets beyond clean accuracy. Robustness analysis (noise, channel dropout), interpretability via Attention-Aware Layer-Wise Relevance Propagation, and expressiveness through block-wise probing. Findings: no single model dominates all failure modes; models focus on task-appropriate brain regions but decode corrupted content poorly.
Comprehensive study of AI-assisted research systems through April 2026. LLMs excel at structured, retrieval-grounded, and tool-mediated tasks but remain fragile for genuinely novel ideas and scientific judgment. End-to-end autonomous systems have not yet consistently reached major-venue acceptance standards. Human-governed collaboration is the most credible deployment paradigm.
CLUES, a framework for clinical Text-to-SQL, decomposes semantic uncertainty into ambiguity and instability scores using the Schur complement of a bipartite semantic graph matrix. Tested on AmbigQA/SituatedQA and a clinical benchmark, it outperforms Kernel Language Entropy and enables efficient triage: 51% of errors in 25% of queries.
ShareChat is a corpus of 142,808 conversations (660,293 turns) collected from ChatGPT, Perplexity, Grok, Gemini, and Claude between April 2023 and October 2025. The dataset preserves native affordances (citations, reasoning traces, code artifacts) across 95 languages and enables analysis of cross-platform differences in user satisfaction, citation strategies, and response latency.
OCCAM is a framework for explaining black-box image classifier decisions through causal visual concepts. It discovers concepts in open-set manner, localizes them via text-guided segmentation, and measures causal contribution through object-level interventions. OCCAM aggregates interventional evidence to induce a structured ontology revealing concept dependencies and systematic model biases.
Study reveals an 'unlearnability' phenomenon in Reinforcement Learning with Verifiable Reward (RLVR) for LLMs. Some hard examples remain unlearnable even with correct rollouts. Cross-example gradient analysis shows fundamental representation flaws: low gradient similarity and ungeneralizable reasoning patterns. Data augmentation fails to improve gradient similarity.
QSTRBench is a benchmark evaluating LLMs' ability to reason with qualitative spatial and temporal reasoning (QSTR). It covers 9 calculi (Point Algebra, Allen's Interval Algebra, RCC-5/8/22, etc.) with composition tables, converse relations, and conceptual neighbourhoods. Tested models outperform guessing but none answer all questions correctly. RCC-22 proves most difficult.
ProRL is a programmatic reinforcement learning framework for combinatorial optimization (job shop scheduling). It generates interpretable policies as human-readable programs via a domain-specific language (DSL-S), exploring the program space through local search and Bayesian optimization. Outperforms classical heuristics and DRL baselines with minimal training episodes.
DoublyCal, a double-calibration framework, improves LLM reliability by quantifying epistemic uncertainty in retrieved evidence and reasoning. A lightweight proxy model generates Knowledge Graph evidence with calibrated confidence, guiding a black-box LLM toward more accurate and well-calibrated predictions.
Theoretical paper formulating multi-step reasoning as s-t connectivity on knowledge graphs. Shows phase transition: if pre-training knowledge is fragmented into small components, augmentation requires Ω(√n) queries; once density threshold is crossed forming a giant component, constant expected queries suffice.
EvolveR is a framework enabling LLM agents to learn from their own experiences through a closed-loop lifecycle. It combines offline self-distillation (synthesizing interaction trajectories into reusable strategic principles) and online interaction (actively retrieving distilled principles to guide decisions). Tested on complex multi-hop QA benchmarks, it outperforms existing agentic baselines.
Study of 124 countries covering 99% of global GDP. Task-based automation exposure measure: 3.3% in South Sudan to 61.6% in China. Distinguishes labor-substituting vs labor-augmenting automation. AI more prevalent in substitution in low-income countries, augmentation in high-income. Women disproportionately exposed to substitution.
A randomized controlled experiment shows LLM access significantly increases average performance, but gains are unevenly distributed. AI Interaction Competence (ability to elicit, filter, and verify outputs) predicts benefits, not GPA. A scaffolding intervention (conceptual maps) reduces outcome variance.
FUNCanon breaks down long-horizon manipulation tasks into action sequences (actor-verb-object) and canonicalizes objects by functional affordances using VLM cues. FuncDiffuser, an object-centric and action-centric diffusion policy, learns on aligned data to generalize across object categories and enable cross-task behavior reuse.
FediLoRA introduces a federated LoRA fine-tuning framework for vision-language models (VLLMs) addressing imbalanced LoRA ranks from heterogeneous resources and missing modalities from user errors or device failures. The method combines simple averaging with structured editing, validated on general-domain and medical-domain benchmarks.
Theoretical investigation of loss of plasticity (LoP) in deep learning under non-stationary environments. Authors identify two primary mechanisms: activation saturation and representational redundancy creating traps in parameter space. Paradox: properties promoting static generalization (low-rank representations) worsen LoP in continual learning.
OPERA is a retrieval-augmented generation (RAG) architecture coupling planning and execution via reinforcement learning. A Goal Planning Module decomposes complex questions into sub-goals, executed by a Reason-Execute Module with specialized components for reasoning and retrieval. Training uses MAPGRPO, a GRPO variant. Superior results on complex multi-hop benchmarks.
Novel data selection strategy for LLM alignment based on DPO implicit reward gap. Method selects harder examples (smaller reward gaps) and achieves superior performance with only 10% of original data across multiple benchmarks.
EvoSynth, an autonomous multi-agent framework, optimizes jailbreak attacks in executable code space rather than prompt space. The system iteratively evolves and self-corrects code-based attack algorithms. Results: 85.5% Attack Success Rate against Claude-Sonnet-4.5, 95.9% average ASR across evaluated targets.
DuIVRS-2 is an LLM-based Interactive Voice Response system deployed at Baidu Maps for large-scale POI attribute acquisition. Using FSM-guided data augmentation, selective generation, and Chain-of-Thought mechanisms, the system processes 0.4 million calls daily with 83.9% Task Success Rate and 130ms latency.
HTSC-2025 is an open-source benchmark of high-temperature superconducting materials discovered 2023-2025 (X₂YH₆ systems, MXH₃ perovskites, M₃XH₈, BCN-doped cage structures, 2D honeycomb). Addresses the lack of standardized datasets for fair comparison of AI algorithms predicting critical transition temperatures.
PAIR is an internal reward model for multi-step LLM training via GRPO. It combines a hidden-state probe (belief consistency) with a lightweight attention head to generate dense step-level reward signals without external model calls or ground-truth dependencies.
arXiv paper on supervised fine-tuning (SFT) effectiveness for LLMs. Authors show SFT primarily removes noise-like token interactions but rarely acquires reliable new ones. The denoising phase is extremely brief; continued fine-tuning introduces overfitted interactions. Implications for early stopping and LLM training.
Comparative study of 11 LLMs (GPT-4o, Gemma-3, Phi-4, etc.) across 10 common workplace tasks. GPT-4o delivers superior performance but at higher cost and environmental footprint; smaller models (Gemma-3, Phi-4) achieve strong results with better efficiency. Advocates task-aware sufficiency assessments over performance-maximizing benchmarks.
arXiv paper argues current LLM safety evaluations lack robustness due to small datasets, methodological inconsistencies, and unreliable setups. Systematically analyzes the evaluation pipeline—dataset curation, automated red-teaming, response generation, LLM judges—and proposes guidelines to reduce noise and improve comparability of attack/defense research.
EXG is an experience graph framework for self-evolving LLM-based agents. It organizes successes and failures into structured, relational representations, enabling real-time cross-task experience reuse and offline reuse as external memory. Tested on code generation and reasoning benchmarks, EXG outperforms reflection and memory-based baselines.
SAPO improves generative recommendation by aligning reinforcement learning optimization to individual reasoning steps. Instead of assigning a single advantage to the entire response, SAPO computes separate group-relative advantages for each reasoning step and SID token, stabilizing training and outperforming baselines across three real-world datasets.
Guided Topology Diffusion (GTD) uses graph diffusion models to dynamically generate optimal communication topologies for multi-agent LLM systems. The iterative framework, guided by a proxy model predicting multi-objective rewards (accuracy, utility, cost), adapts topologies to tasks without gradient-based optimization, outperforming static approaches.