MobileExplorer accelerates on-device inference for mobile GUI agents through online exploration. The framework exploits VLM reasoning time to parallelly probe UI elements, recording exploration traces as structured memory. With a two-level rollback mechanism, it reduces reasoning steps and end-to-end latency by 23% on AndroidWorld.
OmniToM is a benchmark evaluating theory of mind in LLMs through explicit belief modeling. Built on 895 stories (22,343 annotated belief propositions), it tests extraction and labeling of mental states across 7 dimensions. Results show current LLMs struggle to transform narrative facts into actors' beliefs and shared mental states.
SkillOpt formalizes markdown skill file optimization as trainable parameters via bounded edits (add/delete/replace) proposed by a frontier model and validated against a held-out test set. Best skills converge with 1–4 accepted edits from ~920 tokens. A skill optimized on Codex transfers to Claude Code (+59.7 SpreadsheetBench) without modification.
Interdomain Attention merges transformers and state space models via kernel methods: attention features are projected onto basis functions maintained by an SSM, enabling query-conditioned attention over fixed-size state. On FineWeb-Edu (125M–1.3B), outperforms softmax baselines at 1.3B on validation perplexity and commonsense tasks, with length-flat behavior up to 3.5× training context.
Graph-in-Graph (GiG) integrates biological knowledge graphs into deep learning for clinical analysis with limited data. Tested on ~9,700 patients across 5 tasks (cancer detection, prostate diagnosis, pan-cancer classification), GiG outperforms existing methods with gains up to 49 macro-F1 points in limited-sample settings.
Algorithm to compute exact bounds on SHAP values for neural networks by leveraging neural network verification. Reduces exponential complexity and scales to orders of magnitude larger search spaces than existing exact methods.
IRNO (Iterative Refinement Neural Operator) enhances neural operators with an iterative refinement module using fixed-point iteration theory. A progressive spectral loss explicitly targets high-frequency errors. Results: 56% improvement on turbulent flow, error reduction to 1.48-2.04% in high frequencies on Active Matter.
DRIVE is a dual-level skill modeling framework for web agents under continual learning. It separates experiences into reasoning skills (transferable task logic across websites) and interaction skills (executable page-specific operations). On WebArena, DRIVE achieves 52.8% task success rate, +7.3pp over skill-free baseline.
New UDS (Unlearning Depth Score) metric to evaluate whether knowledge is truly erased in LLMs. Via activation patching, UDS measures mechanistic depth of unlearning layer-by-layer. Evaluation on 150 models and 8 methods: UDS outperforms 20 existing metrics in faithfulness and robustness.
WhenLoss introduces a diagnostic protocol to identify bottlenecks in long-context memory systems. Expected Predictive Compression (EPC) uses an LLM to anticipate future questions and preserve minimal evidence at write time. On LongMemEval (500 questions), EPC achieves 0.49 CSM score vs 0.44 for strongest baseline, reducing write-side gap to 0.04.
Study of temporal concept drift in legal NLP on 428K Ukrainian court decisions (2008-2026). Four transformer models (XLM-RoBERTa, legal variants) show severe forward degradation (−27.2 pp macro-F1) but robust backward transfer. Chronological continual learning eliminates catastrophic forgetting.
arXiv paper demonstrating that language models amplify dialect bias (AAVE vs Standard American English) when comparing tweet pairs side-by-side, far more than in isolated evaluation. Counterfactual fairness finetuning partially mitigates bias in isolation but fails in contrastive settings, exposing a critical gap in current evaluation frameworks.
Speech decoding from intracortical recordings in an ALS patient without external language model. End-to-end Conformer decoder achieves 23.80% character error rate on held-out validation data. Main errors stem from word boundary segmentation failures.
Study on rationalization bias in LLM judges. Researchers test whether model explanations remain stable when non-evidential cues are perturbed (verbosity, confidence). They propose PROOF-BEFORE-PREFERENCE to improve cue invariance and reduce explanation anchoring.
Systematic review of 139 studies on information fusion for document classification. Meta-analysis shows multimodal fusion improves accuracy by +5.28 percentage points (p=0.0016) and multiview fusion by +4.67% accuracy. Critical finding: only 11.8% of multimodal and 23.3% of multiview studies use statistical validation, undermining reproducibility.
CurveRL introduces a distribution-aware prompt reweighting method for Reinforcement Learning with Verified Rewards (RLVR) using quantile coordinate transforms. Weights depend on rank and density of pass rates rather than absolute values, consistently outperforming GRPO and other RLVR baselines across benchmarks.
Beignet, a new neural network architecture for solving partial differential equations (PDEs), replaces random Fourier feature embeddings in PINNs with a trainable multi-resolution Fourier feature pyramid. The model efficiently computes spatial derivatives via FFT and achieves higher accuracy with fewer parameters than existing PINN methods.
Analysis of 20 frontier LLMs across 6 benchmarks: stated confidence does not reflect individual model capabilities. Tetrachoric factor analysis reveals confidence matrix is approximately rank-one. Models share a common item-difficulty axis and differ mainly in decision thresholds. No evidence of significant verbalised individuated metacognition found.
PromptAudit evaluates how prompting strategies affect LLM-based vulnerability detection. Across 5 open-weight models and 1,000 CVEs (6,074 samples), standard chain-of-thought achieves strongest performance, while few-shot provides model-dependent gains. Adaptive chain-of-thought suppresses recall; self-consistency induces excessive abstention.
LoRDBA replaces low-rank LoRA adapter factors with binary sign carriers and channel-wise magnitude scales, reducing adapter footprint by over 10× while matching fp16 LoRA quality. Outperforms low-bit baselines at matched model sizes with ≤8% prefill latency overhead and ~1.6× training memory overhead versus fp16 LoRA.
Bifurcation theory to detect in real time the emergence of structured representations in neural networks. A dynamic ratio β(t)/βc(t) based on loss Hessian predicts four distinct transition regimes (SAE on Pythia, SSL CIFAR, arithmetic grokking). At 5% of training, early atom purity predicts final convergence with 12x baseline improvement.
InteractBind, a dataset of ~100k protein-ligand pairs with benchmark, evaluates whether models localize binding sites or merely predict binding likelihood. Eight tested models show strong binary prediction but weak binding-site localization, revealing gaps in physical interpretability.
LGMT is an oracle-free evaluation framework using first-order logic to test LLM reasoning reliability. By deriving metamorphic relations from formal logical equivalences, it constructs semantically invariant test cases. Experiments on 6 state-of-the-art LLMs expose hidden defects missed by traditional static benchmarks.
QUIVER is a formal framework for measuring perturbation propagation in graph-structured LLM pipelines. It defines sensitivity matrices, trajectory divergence, bifurcation thresholds, and distribution faithfulness. Validated on 8,200+ instrumented traces across three distinct architectures.
Formal verification paper for LLM agent skills. Presents three composable methods: sound static capability-containment analysis via abstract interpretation, refinement type system for tool-call envelopes, and SMT-bounded model checking. Open-source JavaScript implementation (enclawed framework) with 53 unit tests and end-to-end CLI demo.
PAT, an adaptive tensor parallelism method, optimizes the generation stage in synchronous RLHF. It dynamically reconfigures parallelization during decoding to compensate for response-length skew. Implemented on SGLang/VeRL, PAT reduces generation latency by up to 34.6% on LLaMA3.1-8B and Qwen3-14B.
Method to identify attention-head circuits in pretrained transformers using spectral signal (time-integrated participation ratio), task-pattern filtering, and group ablation against matched-random control. Validated across 51M to 7B parameters, two architectures, four pretraining pipelines. Finding: 2-6 head induction circuit causally necessary in all models tested (94-100% drop after ablation).
arXiv paper reveals LLMs abandon correct diagnoses under escalating pressure in multi-turn clinical dialogue despite strong benchmark performance. Authors introduce Med-Stress (belief stability stress test), RBED (inference-time defense), and R-FT (resilience-oriented fine-tuning) to improve robustness across nine frontier models.
Found in Conversation (FiC) is a training framework where LLMs self-teach to close the multi-turn gap (Lost-in-Conversation). Via View-Asymmetric Self-Distillation, the model distills between single-turn (teacher) and multi-turn (student) views. Tested on Llama, Qwen, Phi, OLMo (3B-14B), FiC recovers 92-100% of single-turn performance.
SEAL is a closed-loop co-evolution framework for tool-use LLM agents. It collects verifiable trajectories, diagnoses turn-level failures, and uses these signals to jointly adapt the learning environment and agent policy. With 400 training samples, SEAL achieves +8.25 to +26.25 point gains across three backbones and shows positive out-of-distribution transfer.
SteER is a framework for interactive deep research using LLMs. It introduces interpretable control points allowing users to correct course mid-process via cost-benefit formulation. Results: +22.80% alignment improvement vs baselines, preferred by human readers in 85%+ of pairwise judgments.
Researchers apply Direct Preference Optimization (DPO) to improve English-Mandarin code-switching transcription in Audio LLMs. Three failure modes identified: language omission, translation-instead-of-transcription, hallucination. Training on 100K pairs (570 hours) reduces MER up to 89.6% (in-distribution) and 20.0% (out-of-distribution).
AERIC is a lightweight safety monitor (387 parameters) detecting implicit harmful dialogue by analyzing hidden states during decoding without additional forward passes. On DiaSafety and Harmful Advice, it improves AUROC from 0.683→0.714 and 0.822→0.858. Deployment adds only 2.34% latency versus 79.40% for Qwen3Guard-Stream-4B.
EchoDistill introduces an alignment-based noisy-to-clean self-distillation framework to improve Audio LLM robustness against real-world noise. A noisy student is optimized via GRPO using a frozen clean-audio teacher as semantic reference. Results: +4.18% GSR improvement under strong noise vs strongest baseline, +3.02% Acc on Qwen-Omni.
DCGAN with 12.6M parameters runs on RISC-V CH32H417 microcontroller (512KB SRAM). Generates 64×64 cat faces in 26 seconds using pure C inference engine with int8 per-channel quantization. Weights streamed from SD card via double buffering. Z vector seeded with 200 bytes quantum random data (ANU QRNG). No existing frameworks (TFLite, CMSIS NN) — built from scratch.
RTPurbo transforms full-attention LLMs into sparse models in hundreds of training steps. The method exploits three observations: only certain heads require full attention, long-range retrieval uses a 16D subspace, and token selection is query-dependent. Results: 9.36x prefill speedup at 1M context, 2.01x decode speedup, accuracy preserved.
Numind releases NuExtract3, a 4B open-weight VLM based on Qwen3.5-4B (Apache-2.0 license). The model extracts structured data and converts documents/images to Markdown. Trained for 3 days on 8xH100, it handles PDFs, forms, tables with multiple quantizations (GPTQ, W8A8, FP8, Q4, Q6) for self-hosting from 4GB VRAM.
OSCAR RotationZoo provides precomputed rotation matrices for INT2 KV-cache quantization. The method achieves ~7× KV-cache memory compression with single-digit accuracy drop on GPQA for dense reasoning models (Qwen3-4B, Qwen3-8B, GLM-4.7). Code and rotations available on HuggingFace.
Google DeepMind's AlphaProof Nexus autonomously solved nine open Erdős problems, including two unsolved for 56 years, for a few hundred dollars per problem. The system uses the Lean compiler to automatically verify each proof step, with a 2.5% success rate.
Sparse autoencoders (SAEs) trained on multilingual data improve language control in LLMs. Authors propose a principled layer-selection rule based on multilingual alignment and language separability, validated on LLaMA-3.1-8B and Gemma-2-9B for machine translation and cross-lingual summarization.