3147 articles
Paper introduces parallel context compaction for long-horizon LLM agents to address latency and unpredictability of sequential summarization. Enables fine-grained control over summary volume and targeted prompt engineering per block. Evaluated on HotpotQA and LoCoMo benchmarks across 8B-120B models (dense and MoE architectures).
LLM-based multi-agent systems fail despite correct plan execution when agents misjudge their knowledge (epistemic miscalibration). Authors propose EPC-AW workflow that selects plans stable across agents and refines epistemic state over time using past discrepancies. 9.75% improvement in system-level success.
KPI2KVI transforms natural language service descriptions into Key Value Indicator (KVI) estimates using a deterministic multi-agent LLM workflow. The system elicits missing context, extracts relevant KVI categories, generates service-specific KPIs, collects values through interactive dialogue with intelligent estimation, and computes interval-valued KVIs with traceable explanations.
IDS (Inductive Deductive Synthesis) is a multi-agent LLM system jointly synthesizing implementation and formal proof for distributed systems. On 7 key-value store specifications, IDS achieves 7/7 in 6.8h/$106, versus 2/7 for GPT-5.4 and Claude Opus 4.6. Result is 200x faster than expert effort, 17% cheaper than SOTA agents.
EVE-Agent is a self-evolving agent that generates its own questions, answers, and verifiable evidence spans without human annotations. An evidence verifier rewards text spans based on their marginal contribution to correct answers. The training curriculum becomes auditable and trustworthy without external oracles.
GENSTRAT introduces a benchmark for evaluating strategic reasoning in LLMs using procedurally generated card games. Evaluation of 9 models (GPT-5, Claude, Gemini-3.1-Pro) across 36,000+ matches. Methodology decomposes competence across 6 axes and measures local volatility (jaggedness) to diagnose real-world deployments.
SciAtlas is a large-scale academic knowledge graph integrating 43M papers across 26 disciplines, 157M entities, and 3B triplets. It features a neuro-symbolic retrieval algorithm with tri-path collaborative recall and graph reranking to enhance semantic search and reduce inference costs for AI agents in automated scientific research.
ImProver 2 is a neurosymbolic framework for automated proof optimization in Lean 4. A 7B-parameter model trained outperforms orders-of-magnitude larger models and is competitive with mid-tier frontier models. The scaffold exposes formal structure alongside lightweight informal abstractions.
Gen-ROTDA, a robust optimal transport method, adapts Citi Bike demand prediction models across years (2021-2026). It transfers residuals rather than raw demand and uses a label-preserving feature generator. Gen-ROTDA achieves lowest MAE on the 2025-2026 task and outperforms non-robust OT variants under abnormal data.
Theoretical study showing that network depth induces implicit low-rank bias, promoting alternatives to neural collapse. Analysis of unregularized deep UFM (unconstrained feature model) training dynamics reveals how depth favors softmax codes over classical structured geometries.
CFD (Computable Fair Division) proposes a framework for fairly allocating GPU and bandwidth resources among AI agents. The Boltzmann-Softmax β parameter controls the efficiency-fairness tradeoff. AHC++ adjusts β in real time to maintain diversity without major throughput degradation. Simulations: 100x agents = +5.5x execution time.
GEMQ introduces global expert-level mixed-precision quantization for MoE-LLMs. The method uses global linear-programming formulation to estimate expert importance and fine-tunes routers to adapt routing to quantized experts. Results: significant memory reduction and inference acceleration with minimal accuracy loss.
SF-NorMuon, a schedule-free spectral optimizer, matches or exceeds tuned AdamW on 125M and 772M parameter language models without requiring a predefined learning-rate schedule. Theoretical proof of stationarity guarantee and identification of weight decay as essential for long-horizon stability.
World Machine is a transformer-based generative world-modeling architecture with latent states for time series. It reduces the quadratic complexity of standard transformers and adapts to varying amounts of observed data. Validated on synthetic dataset Toy1D.
DreamerNLplus combines DeBERTa, Random Forest, Llama 3.1, and RAG to model mental health dynamics from social media timelines (CLPsych 2026). Ranks 2nd in summarization, 1st in Improvement, 3rd in Deterioration. Code released.
Study showing task-relevant information is distributed non-monotonically across layers in foundational models. Introduces LOES (Layer-wise Optimal Embedding Selection), a spectral method identifying task-discriminative subspaces, and GeoReg, geometric regularization enforcing simplicial structure on class manifolds. Consistent improvements across architectures and modalities.
HawkesLLM models semantic uncertainty propagation in multi-agent text simulation systems. A multivariate Hawkes process captures temporal dependencies between text-generating agents, while an LLM generates each new event from compact selected memory. Evaluation on GDELT dataset shows improved late-stage semantic alignment under memory constraints.
An arXiv study shows that many recent unsupervised feature selection methods are outperformed by random feature selection in both performance and efficiency. Authors propose using random selection as a mandatory baseline to evaluate novel methods.
Theoretical study on multiclass classifier calibration. Authors characterize elicitation complexity for discrete orderable properties (mode, rankings) using Lipschitz continuous properties as intermediary, solving exponential complexity blowup in number of classes.
Prototype-Based Sparse Steering applies Sparse Autoencoders to LLM attention query activations to decompose representations into interpretable features. Gradient-based optimization during inference aligns sparse representations with target behavior prototypes. Validated on Textualized Gridworld (planning constraints) and educational domain (cognitive complexity via Bloom's Taxonomy).
Learned Relay Representations (Relay) enables Masked Diffusion Models to propagate latent information across denoising steps via a differentiable per-token channel trained with truncated BPTT. Applied to Fast-dLLM v2, it outperforms supervised finetuning on coding tasks and reduces inference latency by 32%.
Theoretical study on balancing relational generalization and memorization in learning systems. Authors introduce transitive inference with exceptions task and analytically characterize kernel ridge regression models across representations. Validation on pretrained language models shows successful generalization depends on representational geometry, with systematic errors predicted by theory.
On 1-3B models, CoT in arithmetic relies on a positional shortcut: the model simply copies the number in the final position before the answer delimiter, regardless of intermediate reasoning. This strategy accounts for 54-92 pp of accuracy on GSM8K. Replacing that number with an incorrect value collapses performance even with correct steps.
Study showing explicit reasoning (CoT) benefits only specific tasks. Authors propose EDRM, a training-free framework using early-stage entropy dynamics to adaptively route to CoT or direct inference. Across 15 benchmarks and 4 LLMs, EDRM achieves 41–55% token reduction while improving accuracy up to 4.7%.
Method to quantify uncertainty in language models by analyzing layer-wise representation trajectories. Eleven geometric features extracted from MLP updates outperform maximum softmax probability (MSP) by up to 21 AURC points, revealing where and how errors emerge across depth.
Tensor Cache introduces a two-level cache for transformers: sliding-window local attention (L1) plus fixed-size outer-product fast-weight memory (L2) storing evicted KV pairs as a matrix. A learned gate fuses outputs. Improves memory-quality tradeoff on long-context models.
Paper formalizing cultural adaptation for LLMs in political discourse analysis. Identifies English dominance bias and systematic failures across linguistic/institutional contexts. Proposes evaluation matrix (cultural fidelity, calibration, democratic safety) and methodologies: participatory datasets, culturally-aware transfer learning, culturally-measurable benchmarks.
Two-stage federated recommendation pipeline for mobile devices: collaborative filtering on non-sensitive app-context data in cloud, then on-device re-ranking with sensitive mobile signals. Validated on MovieLens, UCI HAR, and proprietary dataset. Production-ready Kotlin Multiplatform library for Android/iOS.
ClimateChat-300K: dataset of 299,329 public Facebook posts on climate change (May 2020–May 2024), collected via CrowdTangle. 41 metadata features, 26,000+ global pages. Topic modeling and sentiment analysis identify 10 themes across 5 domains; emotionally charged and visually rich content drives highest engagement. Open resource for studying polarization and misinformation.
MaR (Metacognition-as-Reward) is an RL framework improving LLM reasoning via two dimensions: metacognitive knowledge (identifying task-relevant information) and metacognitive regulation (planning the reasoning process). Tested on 22 benchmarks, Qwen3.5-9B + MaR achieves up to 7.7% gain over base model and 11.0% over vanilla DAPO, surpassing GPT-OSS-120B on average.
Cross-lingual red-teaming study of four MLLMs (Claude Sonnet 4.5, GPT-5, Pixtral Large, Qwen Omni) showing jailbreak vulnerability varies by language. Role-play attacks less effective in Mexican Spanish, visual attacks more effective. Safety rankings do not transfer across languages.
SCID-anchored benchmark of 555 semi-structured interviews evaluates 5 LLMs (GPT-4.1 Mini, GPT-5 Mini) on psychiatric screening (anxiety, depression, PTSD). Accuracy 0.49–0.86, MCC 0.16–0.38. False negatives reveal models downweight symptoms when functioning is preserved or social support present, requiring clinical validation before deployment.
Audit of 11 long-context reasoning benchmarks finds none jointly control task position, filler content, and context length. Evaluation of 9 LLMs using Context Rot Evaluation (CRE) reveals sharp accuracy drops when target task moves from end to middle (e.g., Mimo-v2-Flash -88pp at 64K). Newer model releases show reduced positional vulnerability.
Comparison of Structural Topic Models (STM) and BERTopic for analyzing short open-ended survey responses. BERTopic produces higher topic coherence, strengthened by contextual augmentation (strategy introduced to enrich very short responses). STM offers better support for inferential covariate analysis, BERTopic for interpretability.
Unified framework for cost-performance optimization in LLM context management. Jointly evaluates task performance, token cost, and preprocessing reuse on 5,000 HotpotQA instances. Reduces effective token usage by 25% at comparable performance (F1≈0.78) and achieves 50% lower token cost with memory compression versus full-context prompting.
Paper on aircraft disassembly scheduling for end-of-life aircraft. Proposes Constraint Programming and MIP models to handle thousands of tasks with precedence constraints, technician certifications, aircraft balance, and space limitations. Tested on real instances up to 1450 tasks from industrial partner.
Sparse autoencoders decompose GPT-2 XL and Llama-3.1-8B into 16K-32K interpretable features per layer. Semantic features alone recover 94% of peak encoding performance (r=0.285) and align with known cortical semantic organization (ρ=0.72, p<0.001). Results generalize across English, Chinese, and French.
Empirical study on curriculum effects for RL memory agents in multi-session dialogue with external memory banks. Three training conditions tested (LoCoMo only, LoCoMo + LongMemEval, LongMemEval only) show curriculum composition shapes specialized skills rather than uniform performance scaling. Mixed curriculum achieves strongest overall F1.
Researchers reveal that LLM-generated texts contain hidden human-like spans that complicate detection. They propose a stacked model-agnostic framework using a hard-EM procedure to iteratively filter human-like subsequences and enhance existing detectors, working also in training-free mode.
Method using aligned bipartite graphs and graph neural networks to detect hallucinations in LLMs. Trains a GNN on alignment structure between source documents and model outputs. SOTA results on 4 hallucination and QA datasets, outperforming GPT-4o.
Comparative study of 7 LLMs (Gemini, Claude, GPT) to estimate professional expertise from Slack logs. On 27,188 messages from 43 users, Gemini 2.5 Flash achieves lowest error (MAE 21.13%). Accuracy depends only weakly on message volume.
Study of verbatim memorization during Fill-in-the-Middle (FIM) pretraining on Llama 3.2. FIM recovers more short or partial spans compared to standard LTR, with extraction growing linearly with repetitions. Suffix context is insufficient: memorization remains anchored in prefix context.
RAS (Reflection-Augmented Scaling) improves Cypher query generation by leveraging database error messages through in-context learning. Across three Neo4j datasets and five code-specialized language models, RAS reduces Query Execution Error Rate by 41–50% (n=5), outperforming independent resampling (32–38%).
QASC (Query-Adaptive Semantic Chunking) improves document segmentation for RAG by integrating user queries at chunking stage. Using cosine similarity scoring, contextual window expansion, and chunk-level aggregation, QASC achieves F1=0.85, a 18-27% relative improvement over fixed chunking and 8-12% over semantic/agentic methods on 100 technical documents and 200 queries.
Knowledge distillation framework for Text-to-SQL in low-resource settings. Constructs knowledge base (schema semantics, abbreviations, business logic) injected during training and inference. Generates contextually grounded synthetic data. Evaluated on 7 benchmarks: improves open-source and closed-source LLMs, especially on domain-specific datasets.
TPA (Think, Plan, Ask) is a multi-agent framework that guides LLMs to proactively select questioning strategies for assessing Social Language Disorder (SLD) traits in autism. Tested on 484 clinical episodes (ADOS-2), TPA achieves 82.1% SLD trait coverage vs 65.5% for clinicians, with superior diagnostic efficiency (AUCC: 0.628 vs 0.458).
Study of 16 language models (1.5B–72B parameters) showing representational convergence does not extend to reasoning processes. Models align more on collectively failed problems (CKA=0.897) than solved ones (CKA=0.830). Post-decision representations diverge sharply (CKA=0.274), and shared information exerts minimal causal influence (1.5–5.5% flip rate).
Lepton, a fine-tuned BERT classifier on bert-base-chinese, predicts whether a title in a Classical Chinese wenji table of contents is a personal letter or a preface. Trained on 5438 hand-labeled titles from late-Ming and early-Qing literati, the model identified ~55,000 letters across mid-Ming to early-Qing wenji for the Ming Letter Platform.
PCSP, a single RL policy conditioned on frozen LLM embeddings, controls 300 NPCs with distinct personas. Achieves 17x above-chance zero-shot identification, ρ=0.73 semantic-behavioral alignment, 22x faster than LLM-as-policy baseline. Deployed in UE5 on 64 agents with low failure rate.
RMA is a multi-agent system for solving research-level mathematical problems. It decomposes proof solving into specialized modules (problem analysis, literature search, verification) coordinated by initializer, proposer, and verifier agents. On the First Proof benchmark (10 problems), RMA solves 8/10 and outperforms GPT-5.2R and Aletheia.
ThriftAttention uses mixed precision (FP16/FP4) for long-context attention on Blackwell GPUs. By selecting 5% of critical query-key blocks in FP16 and computing remaining blocks in FP4, the method recovers 89.1% of FP16 performance while maintaining FP4 efficiency. Code released.
Brain-LLM alignment depends on training language dominance, not inherent English properties. Test on 112 participants (English, Chinese, French) with 7 LLMs: a Chinese-dominant model (Baichuan2-7B) reverses alignment gradient. Typological distance and tokenization fertility explain remaining variation.
GPLD adds gradient-penalized latent dynamics regularization to DreamerV3 to encourage smooth transition learning in latent space. Tested on DeepMind Control, GPLD improves sample efficiency, with strong gains on complex locomotion and quadruped tasks.
SEELS, a Windows desktop app for local LLMs, lets users correct model replies via a « Teach » button that accumulates corrections into a JSONL corpus, then triggers PEFT LoRA fine-tuning without terminal access. Includes local STT/TTS (Whisper/Piper), hardware dashboard, 0.6B model pre-trained on 110 examples. Free stable version; pro tier (image/video gen, MCP) and max tier (workflows, multi-GPU) in roadmap.
llama.cpp features a KV cache optimization that re-sends generated tokens to cache instead of waiting for next prompt, improving responsiveness. User reports latency reduction from 5-30s to near-instant on Qwen 3.6-35B with RX 7900 XTX (~100 tps).
Sponsio introduces a deterministic contract layer for LLM agents in production. Operators declare invariants in YAML compiled to AST evaluated per tool call. ODCV-Bench benchmark (12 frontier LLMs × 80 trajectories): unguarded models violate in 11.5%-66.7% of runs; with Sponsio, 95.6% misalignment avoided on average.
Hugging Face clarifies AI agent terminology: distinguishing harness (execution infrastructure), scaffold (coordination structure), and agent (autonomous system). Essential definitions to avoid confusion in the ecosystem.
OpenAI partners with Grupo Folha and Grupo UOL to integrate trusted Brazilian journalism into ChatGPT. Content will be attributed with transparency.
Datasette 1.0a30 introduces a customizable "Jump to..." menu accessible via the `/` key. The new `jump_items_sql()` plugin hook allows plugins to add their own items to the searchable menu.
Datasette-agent 0.1a4 integrates an agent chat interface into the Jump menu (/ key) using the new makeJumpSections() JavaScript hook from Datasette 1.0a30. Enables natural language database queries directly from the UI.
hipEngine is an open source (AGPLv3) LLM inference engine optimized for RDNA3 (RX 7900 XTX, W7900). Written in Python with HIP/C++ kernels, it runs Qwen 3.6 MoE faster than llama.cpp on prefill (2718 tok/s at 512 tokens vs 2436 for GGUF Q4_K_S). Near-lossless INT8 KVCache enables 256K context in <24GB.
MergeNB is a VS Code extension to resolve merge conflicts in Jupyter notebooks. Built as an alternative to nbdime, it features a web UI and will be expanded to work as a git mergetool this summer.
A researcher questions hyperparameter selection practices for non-contrastive self-supervised learning (BYOL, JEPA, data2vec). They highlight that non-monotonic losses make it hard to evaluate what is actually learned, and that criteria like RankMe (based on effective rank of embeddings) become ineffective once integrated into the loss function.
Release of datasette-fixtures 0.1a0, a plugin leveraging the new datasette.fixtures.populate_fixture_database() API introduced in Datasette 1.0a30. Enables creation of fixture databases for plugin test suites.
Method to dynamically generate custom interactive textbooks using LLMs. Recursive approach that adapts educational content in real-time based on learner needs.
Method to dynamically generate custom interactive textbooks using LLMs. Recursive approach that adapts educational content in real-time based on learner needs.
User runs Qwen 3.6-35B-A3B-MTP on GTX 1060 6GB via LMStudio. Setup: Q4_K_XL quantization, 131k context, 41 layers GPU-offloaded, prefill 130-150 tps, decode 16 tps. Usable for chat on legacy hardware.
Armin Ronacher (Pi creator) denounces LLM-generated bug reports poorly prompted against his open-source project. These reports contain inaccurate yet confident conclusions, fake minimal reproductions, and wrong root cause guesses. He requests contributors limit issues to observed facts: command run, expected outcome, actual outcome, exact logs.
Critique of using Claude for software architecture. The article challenges the trend of delegating architectural decisions to an LLM, arguing that Claude cannot replace human expertise in system design.
Comparison of two IBM Granite Docling models for OCR: granite-docling-258m vs granite-docling-2stage-258m. The 2stage version introduces dynamic prompts that precompute layout objects, improving robustness on out-of-distribution data.
Simon Willison used Claude to recreate Mad House, a 1980s game from Usborne's « Creepy Computer Games » book (1983), as an interactive JavaScript/HTML version with retro interface. UK publisher Usborne released free PDFs of its 1980s computer books.
Memory now accounts for nearly two-thirds of AI chip component costs. This trend reflects growing bandwidth and storage requirements for increasingly large models.
BitCPM-CANN demonstrates native ternary (1.58-bit) quantization-aware training on Huawei Ascend NPU. Four models (0.5B–8B) retain 95.7–97.2% of full-precision performance across 11 benchmarks (reasoning, GSM8K, BBH). Training overhead: 4.5%. Weight memory reduction: 8×, 6× end-to-end. First 1.58-bit training system scaled to 8B on domestic NPU.
Companies are rebranding existing divisions or services with 'AI' labels to capitalize on tech hype, without substantial innovation. 'AI washing' phenomenon mirrors greenwashing, where marketing outweighs substance.
DeepSeek makes permanent a 75% discount on its flagship AI model. The price cut aims to boost adoption and competitiveness against other AI providers.
JetBrains and Microsoft release official Kotlin support for Visual Studio Code in alpha. The extension provides code completion, navigation, and debugging for the Kotlin language.
ByteDance Seed demonstrates that a 7B model answers questions on long, image-heavy documents more reliably than much larger models, even on documents 4× longer than training data. Key finding: learning via question-answering outperforms text transcription approaches.
DeepSeek releases Reasonix, a native coding agent optimized for high caching and low cost. The model leverages DeepSeek's reasoning capabilities with a specialized architecture for code generation tasks.
Study on the fragility of LLM agents in backend code generation. Constraints imposed on models degrade progressively, reducing their ability to respect technical specifications. Critical issue for production systems.
Demis Hassabis (DeepMind) claims humanity is "standing in the foothills of the singularity," while Yann LeCun argues current AI systems aren't genuinely intelligent. Oriol Vinyals (Gemini co-lead) takes a middle position: today's models would have looked like AGI seven years ago, but they still can't learn from experience or produce real breakthroughs.
Developer builds cgo-free CUDA binding for Go using purego to load libcuda.so at runtime. Solves thread affinity issues with runtime.LockOSThread and channel-based executor. Early-stage weekend project adding multi-GPU and Graphs support. Repo: github.com/eitamring/gocudrv.
Niels (Hugging Face) announces week 1 of paperswithcode.co, a revival tracking SOTA across AI domains. New features: multiple metrics per benchmark (WER/RTFx for ASR, mAP/FPS for detection), external paper support (GitHub, blogs, BioRxiv), paper lineage (predecessors/successors), new methods (Gated DeltaNet, Kimi Delta Attention, Mamba-2).
Qwen 3.6-35B in non-MTP version plays DCSS (open-source roguelike) effectively. Practical test on LM Studio with RTX 5090: Minotaur character level 5, 47 HP, multiple enemies defeated. MTP version produces malformed tool calls. Alternative benchmark to official scores.
User reports output quality degradation of Gemma 2B after 30-40 continuous inferences on 4GB GPU (RTX 1650). Responses become shorter, JSON fields missing, sometimes empty. Restarting llama-server fixes it. Possible KV cache or memory fragmentation issue.
A llama.cpp user implements a secure web RAG workflow by enabling native server tools (exec_shell_command) with multi-sandboxing: firejail + dedicated Linux user + Alpine OCI container. Allows Qwen 3.6-35B model to execute wget commands directly from web UI to fetch and analyze content.
cmux is a Ghostty-based macOS terminal with vertical tabs and notifications designed for AI coding agents.
Pi is an AI agent toolkit providing a coding agent CLI, unified LLM API, TUI & web UI libraries, Slack bot, and vLLM pods support.
Anthropic releases an open-source repository of plugins for Claude designed for knowledge workers. Plugins enable integration of Claude into professional workflows.
cc-switch is a cross-platform desktop all-in-one assistant integrating Claude Code, Codex, OpenCode, OpenClaw, Gemini CLI, and Hermes Agent. Aggregates interfaces for multiple AI models and agents.
Modrinth releases its complete monorepo on GitHub. The repository contains the full source code powering the Modrinth mod distribution platform.
Sail is an Apache Spark replacement written in Rust, unifying batch processing, stream processing, and compute-intensive AI workloads.
Plano is an AI-native proxy and data plane for agentic applications, featuring built-in orchestration, safety, observability, and intelligent LLM routing.
Vibe-Kanban is an open-source tool that amplifies productivity of coding agents like Claude Code and Codex through a Kanban interface. Enables managing development tasks with AI agents.
Dexter is an autonomous agent for deep financial research. The GitHub project enables automated financial analysis through a multi-agent system architecture.
Twenty is an open-source alternative to Salesforce designed for AI. The project gains traction on GitHub Trending, positioning open-source CRMs as viable competitors to proprietary solutions.
Presenton is an open-source AI presentation generator with API, positioned as an alternative to Gamma, Beautiful AI, and Decktopus. The GitHub project offers an automated solution for creating slideshows.
Superset is a code editor designed for the AI agents era. It enables running multiple instances of Claude Code, Codex, and similar code models on your local machine.
Trigger.dev is a platform to build and deploy fully-managed AI agents and workflows. The trending GitHub project provides complete infrastructure for orchestrating autonomous agents in production.
Postiz is an agentic social media scheduling tool. The GitHub trending project offers an AI-powered solution for automating content publication across multiple platforms.
Pi is an AI agent toolkit featuring a coding agent CLI, unified LLM API, TUI and web UI libraries, Slack bot, and vLLM pods support.