2731 articles
LinTree improves LLM reasoning by explicitly representing the tree structure of search traces. Researchers show raw access to search history alone fails to reliably outperform LLM-guided heuristic search. Adding parent pointers to explicitly represent the linearized tree structure improves performance and search efficiency on Blocks World, grid Navigation, and Sokoban.
Study of stylistic signatures introduced by LLM alignment. Researchers show post-training creates a detectable AI-like style. They propose PASTA, a training-free method that localizes and ablates this signature during decoding, reducing detection rates across 11 aligned models and 6 AI detectors.
Study on long-term effects of data selection during multi-stage LLM fine-tuning. Authors show that short-term optimal strategies (loss-based, gradient-based, diversity-based) can slow future learning and increase catastrophic forgetting. They propose LHAS (Long-Horizon Aware Selection) to evaluate selection as a global training intervention.
SubsurfaceGen is a GPU-accelerated generator for 3D velocity models and seismic data at field scale. Authors release a dataset of 4,276 2D slices covering 6 geological settings (10 km × 10 km × 6.19 km at 10 m resolution). Evaluation of neural operators on wavefield prediction and end-to-end velocity inversion with out-of-distribution testing.
New approach combining Answer-Set Programming (ASP) and Reinforcement Learning to create logical abstractions of state spaces. Authors reimplement the CARCASS framework (originally in Prolog) using ASP, a fully declarative language, and evaluate it on Blocks World and Minigrid. ASP provides richer modelling for logical representations of Markov Decision Processes.
Theoretical paper positioning diffusion models as part of a family of learning techniques that withhold information and train models to recover it. Author argues destruction-based information withholding is more flexible than hand-crafted techniques, especially in data-scarce settings. Raises exploration challenges and proposes diffusion-native research directions.
Untrained neural networks match early visual cortex better than trained networks. Study on 720 THINGS images and fMRI from 3 subjects shows one training epoch reduces V1 alignment by 25-90% depending on learning rule. Backpropagation degrades most (Δr = -0.080), while predictive coding and STDP preserve alignment better (Δr ~ -0.04).
Zeroth-order sampling method with variance reduction for non-log-concave distributions in black-box settings. Proposes ZO-APMC for inverse problems with generative priors. First non-asymptotic convergence guarantees established.
HypoAgent is a multi-agent framework for interactive abductive hypothesis generation over knowledge graphs. Three coordinated agents (intent recognition, hypothesis generation, root cause analysis) enable multi-turn dialogue and fine-grained diagnosis of failed hypotheses. SOTA on commonsense and biomedical KGs.
SCALE is a self-improving framework for web agents using MLLMs. It employs three adversarial roles (Selector, Predictor, Judger) to autonomously explore agent limitations and expand cognitive boundaries. SCALE-Hop optimizes global planning via graph exploration. A SCALE-20k dataset from 19 real websites with 20k structured demonstrations validates the approach across multiple MLLMs.
Method to assign consistent predictability scores to short trajectory windows on a deterministic-stochastic continuum. GON (Gauge-Fixed Ordinal Network), a temporal convolutional model, resolves cross-system ambiguity via anchor-and-variance objective. Transfer validated on 5 dynamical systems, outperforming scratch training across all window budgets.
AMNESIA is the first large-scale open-source benchmark for machine unlearning in medical LLMs. It contains 70,560 question-answer pairs from 8,820 patient notes across 11 disease categories. The authors evaluate 4 unlearning methods and show that forgetting individual patients erodes knowledge of others with the same condition.
New differentially private sketching mechanism using fast transforms (Hadamard matrices). Combines matrix compression with privacy guarantees for DP linear regression. First fast method for DP ordinary least squares with improved runtime and utility guarantees.
Protocol to evaluate ChatGPT's ability to generate disease-centric biomedical associations. Uses RAG with open-source LLMs for semantic verification and hallucination detection through cross-model majority voting.
Study on LLM teams playing ChGK (collective reasoning quiz). Three strategies tested: Voting, Silent Team (captain observes answers), Talkative Team (captain observes answers + rationales). On 572 questions from 2025, teams outperform single models (+20 points). Best team: 44.23% accuracy, approaching human performance. Sharing rationales mitigates errors.
Bias evaluation of multimodal speech recognition models (audio-visual). Researchers create videos pairing different faces with identical audio and measure transcription accuracy variations. Findings: quality-of-service gaps up to 4.05 word error rate points across gender, ethnicity, and intersections on Whisper-Flamingo and Gemini.
CanLegalRAGBench is an evaluation benchmark for RAG systems applied to Canadian law, based on realistic queries and expert-annotated answers. The study shows open-source embedding models are competitive with closed-source alternatives, but identifies hallucinations in 8-29% of generated answers unsupported by retrieved documents.
arXiv study on LLM security against untrusted inputs. Researchers test whether wrapping untrusted content in mock tool calls improves robustness across 7 models and 3 LLM-as-a-Judge tasks. Finding: the approach fails and typically increases attack success rates, inverting the expected instruction hierarchy.
Study on KV cache prompt redundancy during decoding. Researchers show upper-layer prompt cache can be replaced with chat template scaffolds without significant accuracy loss, revealing redundancy is structural rather than semantic. Results validated across Qwen3, Gemma 3, and Llama 3 families.
Paper proposes an alternative to deep neural networks for LLMs using RBF networks. The model finds the global optimum of the loss function in closed form in a single iteration, eliminating iterative training. High-level overview with case study and comparison to similar methods provided.
DSFM (Dual-Spectral Flow Matching) generates synthetic fMRI time series by combining discrete wavelet transform (DWT) and discrete cosine transform (DCT) with spectral flow matching. The model captures non-stationarity and spatiotemporal dynamics of BOLD signals to improve brain network classification.
Unicorn, a multi-dataset pretraining framework, bridges the trade-off between channel-independent models (scalable but ignoring dependencies) and channel-dependent models (expressive but dimension-bounded). Using a latent prototype codebook, it projects heterogeneous channels into a shared space to learn identity-agnostic, reusable correlation patterns transferable across domains.
A user proposes using HTML as the primary output language for LLM agents to generate interactive and animated content directly in chat via iframes. The approach is tested with Qwen 3.6-27B on dual 3090s.
npm-scan is a supply chain security tool for the npm ecosystem. It detects vulnerabilities and malicious dependencies in JavaScript packages.
A researcher built Mamba-Titan-1.4B-Reasoning (2.54B params MoE) on RTX 3060 by freezing a 1.4B Mamba-1 backbone and adding 8 trainable experts. Trained on DeepSeek CoT traces, the model developed a 'vault door' mechanism: the </think> token isolates at the smallest norm (1.991 vs 4.742 mean) to control latent reasoning termination.
Developer built an MCP server enabling LLMs to explore the Mandelbrot set with rendering tools, presets for interesting regions (Seahorse Valley, Elephant Valley), inspection tools for iteration counts and viewport settings, color palette selection, and HTML gallery generation. Tested with Qwen 3.6-35B. GitHub: openmandel.
A report exposes human rights costs of generative AI systems, including worker exploitation for data annotation, intellectual property violations, and environmental impacts. The study documents unethical practices across the model production pipeline.
MiniMax M3 announced with 1M token context window, advanced coding and agentic capabilities, multimodal support. Positioned at the frontier of high-performance open-source models.
Minimax M3 is rolling out on the API. The model was spotted approximately 15 minutes ago in a screenshot.
A r/LocalLLaMA user recommends investing in GPUs rather than using workarounds for limited VRAM. He reports running Qwen 3.6-27B in Q8 with f16 K/V cache on 2×used RTX 3090s, achieving 128k context length (1399 pp, 104 tg).
Vercel Blob now supports OIDC authentication as default for new projects. Vercel-issued OIDC tokens are short-lived and auto-rotating, eliminating the need for long-lived tokens. Vercel Functions and CLI automatically receive the token.