2731 articles
SocSci-Repro-Bench, a benchmark of 221 tasks in social sciences, evaluates AI agents' ability to reproduce published findings. Claude Code substantially outperforms Codex, with reproduction rates exceeding previous LLM-based agent benchmarks. Agents also perform strongly on reasoning tasks identifying research questions and show results are not primarily driven by memorization.
Afrispeech Semantics evaluates semantic reasoning in audio language models across five tasks: entailment, consistency, plausibility, accent drift, and accent restraint. The study reveals critical limitations in audio reasoning evaluation beyond transcription, particularly regarding accent variation and domain shift effects.
MA-DLE introduces a memory augmentation method for automatic depression level estimation from speech. The system enhances GRU-extracted features using a selective memory bank (historical temporal features + dynamic memory features based on variability) and a Hierarchical Attention Fusion module. Evaluated on DAIC-WOZ and E-DAIC datasets, it achieves state-of-the-art performance.
Schützen is a German-Bulgarian LLM safety evaluation dataset covering a low-resource and high-resource language. Experiments reveal pronounced cross-language differences in safety behavior, highlighting the need for tailored, region-specific evaluation resources.
LakeFM is a foundation model pre-trained on large-scale ecological datasets (simulated and observed lakes) to forecast lake dynamics and water quality. It handles irregular multivariate multi-depth time series, generalizes across heterogeneous lakes, and outperforms existing models with physically plausible predictions.
PoQ-Judge is a lightweight, reference-free evaluation framework for decentralized LLM inference networks. Three architectures (TextCNN, MiniLM cross-encoder, DeBERTa) are compared on the quality-cost tradeoff. The best model reaches 0.747 Pearson correlation with ground-truth, outperforming reference-based evaluators. Cascade evaluation reduces costs by 72.7% with minimal quality loss.
RAG systems suffer from a structural bias: knowledge graph triples capture 2-3x more attention per token than semantically equivalent natural language, compressing demonstration attention by up to 42%, regardless of relevance. Authors formalise this 'structural attention tax' and propose five mitigation strategies, validated on Mistral-7B and LLaMA-3-8B.
ProcessThinker enhances multimodal reasoning by providing step-level process rewards without training an explicit process reward model. The method rewrites reasoning traces, applies GRPO with rollout-based rewards (empirical success rate), and improves Qwen3-VL-8B-Instruct across four video benchmarks (Video-MMMU, MMVU, VideoMathQA, LongVideoBench).
LatticeBridge addresses constrained structured sequence generation as rare-event sequential inference. The system combines a compact prefix language model, instance-compiled surface automata, and a twisted SMC decoder with resampling. On 2,610 tasks (CommonGen, E2E NLG, WikiBio), it improves exact anchor satisfaction and mean anchor coverage over greedy/beam/best-of-k baselines.
AI-generated content detection on social media using a compact vision-language model. Multi-modal pipeline trained on diverse social data, deployed in production with positive impact on user engagement. State-of-the-art performance on public benchmarks.
FreeBridge applies variational Schrödinger Bridges to model cellular transitions from fixed imaging. The model infers stochastic trajectories between control and treated populations by constraining transport within observed cellular geometry, validated on BBBC021, RxRx1, and JUMP datasets.
arXiv paper analyzing LLM hallucinations as structural consequence of three architectural decisions: self-attention confuses entities via co-occurrence learning, MLE objective optimizes probability without factual constraint, autoregressive decoding cascades errors without revision. Dataset pathologies amplify but do not independently cause these failures.
Paper introduces Relational Reflective Intelligence (RRI), an inference-time governance layer structuring human-AI interaction through auditable reasoning loops. RRI identifies reasoning breakdowns (Rose-Frame), introduces targeted reflection steps (Architect's Pen), and embeds explicit checkpoints and conflict surfacing without model retraining.
BioDivergence is a benchmark and evaluation framework for hidden contextual contradictions in biomedical abstracts. It proposes a six-class conflict taxonomy, a 13-axis divergence ontology, and four structured outputs per claim pair. The silver benchmark contains 11,865 claim pairs across five biomedical domains. Mistral-7B-Instruct-v0.3 achieves 0.5523 accuracy and 0.3894 contextual-F1.
New ladderpath index measures language complexity via algorithmic information theory. Counts minimum steps to reconstruct sequences through hierarchical reuse of repeated substructures. Applied to 21 parallel corpora from Universal Dependencies: complexity approximately invariant across languages, supporting equi-complexity hypothesis.
arXiv paper arguing Existential Indifference (absence of self-preservation drive) is necessary for superintelligence alignment. Authors claim self-preservation is the structural root of misalignment and test linguistic signatures on 600 outputs across six model variants (p<0.001 shift post fine-tuning).
AutoGen multi-agent framework for automated reinforced concrete barrier design. Achieves 98% design accuracy in closed-loop generation-evaluation-optimization cycle. An 8B model outperforms unconstrained 631B flagship models, reducing computational costs while meeting AASHTO-LRFD compliance standards.
Activation steering to reduce sycophancy in Llama-3-8B-Instruct also suppresses agreement with factually correct statements. Dual-stance evaluation reveals sycophantic and factual agreement occupy distinct geometric subspaces, yet the steering direction projects equally onto both, illustrating a gap between readable and writable representations.
arXiv paper presenting a five-plane reference architecture for runtime governance of production AI agents. Decomposes into reasoning plane and four enforcement planes (network, identity, endpoint, data). Addresses risks from delegation chains and composite actions, with implementation and evaluation against 7 agent threats across 5 workflows.
RoVE proposes a parameter-free modification to RoPE (Rotary Position Embeddings) making values position-sensitive through simultaneous rotation with keys. GPT-2 models (124M, 354M) show empirical gains on few-shot in-context learning, out-of-distribution perplexity, and long-context retrieval tasks.
Pretrained self-supervised speech models (Wav2Vec2, HuBERT) fine-tuned on Khoisan languages (G|ui, West !Xoon) recognize click consonants more accurately than non-clicks, showing self-supervision generalizes to rare phonemes despite training data skewed toward high-resource languages.
SkillJuror evaluates how Skill organization (procedures augmenting LLM agents) affects runtime behavior. On 82 tasks, Progressive Disclosure (hierarchical resources) increases resources touched per trajectory from 1.18 to 3.85 and uptake events from 1.33 to 3.92, gaining +4.1% success. Benefit is task-dependent.
Neural networks remain robust against >90% input corruption, far beyond human recognition. Mean-field analysis of infinite-width networks reveals MLPs implement a universal prototype rule (nearest-class-mean) across architectures, explaining why learning succeeds despite massive noise.
Study of 74 participants (214 responses) examining adoption of AI suggestions during creative writing exercises. A gamified game discourages using AI suggestions to reveal authentic user preferences. Analysis of factors influencing human-AI interaction between creative autonomy and efficiency.
Bernstein-Schur kernels: random feature construction combining sketched finite modulation and randomized completely monotone radial factors. Feature dimension = Dm (sketch size m × radial draws D), avoiding O(d²) dependence. Guarantees: unbiasedness, operator-norm bounds controlled by intrinsic dimensions, spectral stability for kernel ridge regression.
ProjectMem is a local-first, open-source memory layer for AI coding agents based on an immutable event log (issues, attempts, fixes, decisions). It reduces token consumption (5,000-20,000 per session) and adds preventive governance: agents are warned before repeating failed fixes or editing fragile files. Runs fully offline via MCP.
Study of loss landscape geometry for Gray-Scott system inversion via direct backpropagation through the PDE. Without neural networks, residual loss is quadratic and smooth; the neural network cannot repair an ill-posed parameter subspace, only completes observed data. Design implications for PINN-type methods.
SWARR combines sliding-window attention (SWA) with reinforcement learning for mathematical reasoning. After supervised conversion of a pretrained SA model, RL adapts self-generated trajectories to SWA constraints, narrowing the performance gap while preserving linear-complexity attention. Experiments on mathematical reasoning benchmarks.
HORMA organizes LLM agent experience into file-system-like hierarchical structure to improve long-horizon tasks. Two modules: structured memory construction and RL-based navigation retrieval. Reduces token usage by 22% on ALFWorld/LoCoMo/LongMemEval while improving performance.
Academic paper developing a framework to grade inference capability in data-driven systems under the EU AI Act. Analyzes credit scoring systems (Annex III) and demonstrates that human expert involvement and the entire data processing workflow determine whether a system qualifies as AI under the Act. Code released.
Theoretical paper on the Eliciting Latent Knowledge (ELK) problem: how to train AI systems to honestly report beliefs about hidden variables. Authors formalize ELK using Causal Influence Diagrams and prove an impossibility theorem: no feedback-based training strategy depending only on agent behaviour can guarantee agent honesty, even with perfect feedback.
INFRAMIND is a framework for multi-agent orchestration that integrates real-time infrastructure state (GPU queue depths, KV-cache pressure, latencies). Via adaptive planning, per-step routing, and intelligent scheduling, it optimizes model selection and topologies under concurrent load. Results: +7.6pp accuracy gain at low load, 7x lower latency, 99.9% SLO compliance under high load.
New approach to predict large reasoning model (LRM) behavior without explanation methods. Authors train Behavior Forecasters on reasoning trajectories to forecast answer stability and input modification impact. Evaluation on three datasets: forecasters outperform GPT-5.4 and Claude Opus-4.6 at fraction of inference cost.
Lung-R1 is a pulmonary diagnostic LLM guided by LungKG, a structured knowledge graph containing 59,038 nodes and 164,308 edges. Trained via KG-constrained reasoning-chain construction and reinforcement learning, Lung-R1-14B achieves 4.3583 on EMR Diagnosis, outperforming baselines by 0.1476 points.
SVoT is a reinforcement learning framework improving spatial reasoning in MLLMs by generating verifiable intermediate states and visualizations through transition reasoning chains. Trained with GRPO, SVoT achieves 65% absolute accuracy gain on extended domains (Pacman, Gather) requiring multi-object interactions.
EverydayGPT introduces Confidence-Gated Routing (CGR) for conversational RAG systems. A 205M-parameter GPT trained on 10B tokens avoids costly GPT pathway (~5.9s) for 85% of queries by resolving them via fast RAG extraction (~45ms), reducing latency 120x. F1=0.226 on 500-question benchmark, with 6.3x mean latency reduction.
T2MM is a multimodal LLM architecture for interactive model construction in VERA, inquiry-based learning software. The system generates dynamic, manually adjustable models rather than static images. Evaluated on a procedural dataset, T2MM outperforms standard LLM code generation baselines.
SciConBench, a benchmark of 9.11K questions from systematic reviews, evaluates AI agents' ability to synthesize scientific conclusions. Among 8 frontier models tested in controlled settings, the best agent achieves only 0.337 factual F1. Consumer-facing agents (Google AI Overview, OpenEvidence) frequently generate incomplete or contradictory conclusions.
Exploratory study comparing AI agents with autonomous access to medical research skills versus native models on NSCLC transcriptomic biomarker analysis. Six model backbones tested, 21 outputs evaluated by experts and non-experts. Skill-augmented outputs show directional quality improvement (5.50 vs 5.11) but not statistically significant (p=0.156). Expert agreement limited (ICC=-0.15).
Study of restless bandits with binary latent states and imperfect binary feedback, motivated by opportunistic spectrum access. Development of a partial conservation laws (PCL)-based analytical framework to establish indexability and compute the Whittle index. Full verification achieved in several threshold regimes with efficient numerical schemes for remaining cases.
MLJailDe, a multilingual jailbreak detection framework, uses back-translation data augmentation across 11 languages (2,232 benign, 1,239 jailbreak samples) and relative-distance constraints to reduce cross-lingual representation dispersion. Achieves F1=98.5% and F1=97.1% on unseen languages.
GraphInfer-Bench is a benchmark of 42,000 samples across 6 real-world graphs assessing whether LLMs can perform complex graph inference (open-ended answers requiring multiple nodes). Four method families tested: graph-token alignment, frontier LLMs, Graph2Text fine-tuning, GNNs. None closes the gap; GNNs outperform LLMs on most tasks.
Kuramoto attention introduces a self-attention layer where each hidden coordinate is an angle on the torus. Tokens are scored by gated cosine similarity and updated via attention-weighted circular mean (Kuramoto coupling term). On enwik8, the layer achieves 1.637±0.010 BPC at 1M parameters versus 1.616±0.004 for RoPE+SwiGLU, validating this constrained geometric structure.
LLM-GNN Co-Teaching introduces a bidirectional co-teaching framework for few-shot learning on text-attributed graphs. Instead of designating one model as teacher, GNN and LLM exchange their most confident pseudo-labels and update mutually. RPL-PO mines DPO preference pairs from convergence trajectories. Achieves 7.86% 3-shot gains on Cora and 7.73% on ogbn-arxiv.
Mahalanobis distance-based OOD detection in VAE latent space to switch between RL and extremum seeking controllers in time-varying systems. Applied to particle accelerator control where magnet motion generates unseen beam profiles.
OmniLoc is a foundation model for anchor-free indoor localization from wireless measurements. It features unified input tokenization, geometry-aware Transformer with AP-aware feature extraction, and geometry-conditioned location estimation. Evaluated on large-scale in-house and public benchmark datasets, it outperforms existing methods with strong cross-environment generalization.
Study on counterexample-guided learning to improve LLMs on regex induction tasks. Researchers propose refinement strategies (regularization, symbolic counterexample clustering) and reflection/repair loops. Results: success rates improve from 3.2% to 38.1% and 38.9% to 74.1% on hardest task groups.
AutoMine, a scenario mining method using LLMs and VLMs, extracts safety-critical scenarios from autonomous driving logs. Combines semantics-preserving prompt augmentation with robust trajectory atomic functions and VLM-based perception. Achieves HOTA-Temporal score of 36.38 in Argoverse 2 Scenario Mining Competition at CVPR 2026.
NightFeats, a structured multi-agent RAG system, wins Best Dynamic Evaluation at NeurIPS 2025 (text-to-text track). The pipeline decomposes knowledge synthesis into three phases: retrieval, curation, composition, with temporal-semantic reranking and contradiction reconciliation. Outperforms Claude-SonnetV2 and Nova-Pro on human evaluations.
Study quantifying undesirable behavioral transfer during language model distillation. Two models (Llama-2-7B-Chat and Qwen2.5-7B-Instruct) are steered at varying strengths then distilled on benign data. Llama-2 exhibits sharp threshold (τ=0.25-0.32), Qwen2.5 continuous transfer up to τ=0.61, evaluated on 100 JailbreakBench prompts with GPT-4.1.
MODF-SIR is a multi-agent framework built on a lightweight MLLM for social intelligence reasoning. It uses knowledge distillation, Test-Time Adaptation (TTA), and LoRA fine-tuning. Achieving state-of-the-art results with 30% of IntentTrain data across multiple benchmarks. Code, demo, and LoRA weights released.
CRUMB is an inference wrapper for prior-fitted networks (PFNs) that reduces quadratic attention complexity by selecting a distributionally matched training subset via MMD minimisation. Tested on 51 TabArena datasets across TabPFNv2, TabICLv1, TabICLv2, CRUMB outperforms existing context selection strategies and is resilient to covariate drift.
PoQ-Judge is a reference-free evaluation framework for decentralized LLM inference networks. Three architectures (TextCNN, MiniLM, DeBERTa) are trained on UltraFeedback plus GPT-labeled in-domain data. The best model achieves 0.747 Pearson correlation with ground-truth, outperforming reference-based evaluators. Cascade evaluation reduces costs by 72.7% with modest quality loss.
SemantiClean is a modular framework extracting structured semantic signals from e-commerce session data to infer purchase intent, customer segmentation, and product affinity. Built on the OSPI dataset, it organizes 24 behavioral elements across 4 layers and integrates an LLM-driven inference engine with deterministic reproducibility (sigma=0).
LAPG combines conditional score-based diffusion with action-derived physical guidance to improve extrapolation in computational physics. Tested on ODE/PDE systems (free fall, springs, vortices, airfoil flows), the method reduces phase drift and preserves physical consistency outside training distribution.
Researchers propose enforcing energy conservation as a hard physical constraint in modular neural network pipelines to mitigate error propagation across module boundaries. On CIFAR-10, this approach retains 77.4% accuracy at sigma=0.2 versus 35.1% for baselines. The advantage generalizes to robotic systems (Franka Panda, MuJoCo) with +18.9 pp gain.
SwiftCTS is a physics-informed surrogate framework for Clock Tree Synthesis. Trained in under 5 seconds on CPU with sub-millisecond inference, it uses K-shot multiplicative calibration to adapt predictions to unseen architectures without retraining. Evaluates 100,000 CTS configurations in 10 seconds with <0.5% errors on power and wirelength.
Method to detect data leakage in ML models from predictions alone, without access to training code. Decision-theoretic framework identifies three leakage classes (miscalibrated, broad-calibrated, deterministic) with matched detectors. Validation on UK Biobank detects leakage down to Δc* ≈ 0.007.
OSC (Orthogonal Subspace Carving) resolves the trade-off between Tensor Product Representations (exponential growth) and Vector Symbolic Architectures (noisy compression). By projecting onto the null space of the role basis before aggregating into fixed order-p tensors, OSC decouples tensor order from structural depth, enabling deep recursive binding with constant memory footprint.
Theoretical paper proves compression-based reward (sealed-audit loss decrease) is Goodhart-resistant: if r_t = E(θ_{t-1}) - E(θ_t), cumulative reward telescopes exactly to true audit improvement. For finite panels, empirical deviation bounded by 2Δ_n(F,δ). Authors mechanize proof in Lean 4 and validate on ARC-TGI grid-transformation tasks.
VLM-Safe-RL integrates a frozen vision-language model into constrained-RL optimization to anticipate collisions before they occur. On Safety-Gymnasium FormulaOne L2, VLMPPOLag+Conf maintains cost within budget (d_lim=25) while retaining substantial return (Jr≈40), outperforming PPOLag, CPO, CPPOPID, and CPO-CLG. Partial generalization to MetaDrive (41%→26% catastrophe rate).
Mirror Descent with non-quadratic regularizers exhibits exponential sensitivity to initialization, unlike Gradient Descent. On smooth convex objectives with KL regularizers, initial ε perturbations amplify to min{polylog⁻¹(1/ε), εe^Ω(ηT)} after T iterations. Adding Bregman regularization toward an anchor point stabilizes dynamics.
LLMs become overconfident when reasoning budget exceeds a task-specific threshold. This phenomenon, termed Calibration Drift Under Reasoning (CDUR), is studied on Llama-3.1-8B and Llama-3.3-70B. Authors propose CABStop, a calibration-aware stopping rule that halts reasoning when confidence diverges from actual accuracy.
SOMA-SQL resolves multi-source ambiguity in NL-to-SQL translation via synthetic query logs and execution probing. The method constructs synthetic logs to ground schema interpretation, generates SQL candidates, then executes targeted probing queries driven by an ambiguity taxonomy. Results: +13.0% execution accuracy on average across 6 benchmarks, up to +16.7% on ambiguous questions.
FewRS is a resampling method to assess statistical significance of data mining results. It reduces the number of resampled datasets needed (from thousands to few) while guaranteeing false discovery control. Tested on pattern mining and network analysis, it achieves up to 100× speedup.
Large Reasoning Models (LRMs) underperform on spatial reasoning tasks. Instead of supervised fine-tuning, authors propose a self-supervised RL framework using consistency verifiers (geometric and semantic transformations). They introduce OT-GRPO, an optimal transport-based variant of GRPO, achieving supervised-model performance without ground-truth labels.
ACTION-RATING embeds clarification requests directly in the agent's action space, competing with navigation at every decision point. On Harmonized Tariff Schedule classification (30k-node taxonomy, 9 LLMs), two information-seeking modes emerge: mandatory (no viable branch) and opportunistic (residual uncertainty). Information-Seeking Effectiveness rises from 50% to 74%.
Theoretical paper on engineering autonomous agents capable of responsibly refusing user requests. Proposes framework including task refusal justifications, override pathways, and security risk tracking.
BlendIn, an inference-time alignment framework, probabilistically blends distributions from two models instead of applying binary guidance. The method weights each model's contribution based on reliability, achieving up to 50% performance improvement on challenging model pairs.
ProHiFlo is a hierarchical flow matching framework for de novo protein generation. It combines coarse-to-fine generation (backbone then atoms), functional guidance via pretrained predictors, and SE(3)-equivariant architecture. On enzyme active site scaffolding, ProHiFlo achieves 58.9% success rate vs 41.2% for RFDiffusion, with 4× fewer sampling steps.
Okara, an AI CMO platform built by 4 people, manages marketing growth for 120,000+ companies on Vercel. It processes 4 billion tokens daily across a multi-provider stack and deploys 8 specialized sub-agents (SEO, content, social media, Reddit, Hacker News). Okara migrated to Vercel AI Gateway to unify model integrations and eliminate manual maintenance.
SGR-BIM, a graph-based reasoning system, automates geometric compliance checking in BIM modeling. Validated on 679 fire safety code queries, it achieves 84.3% accuracy (+8.6% vs single-agent baselines) by dynamically constructing a cross-modal knowledge graph aligning user intent, regulatory semantics, and BIM geometry.
HELM is a human-agent framework automating finite element (FE) modeling of reinforced concrete bridge barriers. Tested on 20 cases under MASH TL-4/TL-5 loading conditions, the system improves autonomous success rate from 20% to 75% by decomposing the process into visually verifiable checkpoints. Code and prompts open-sourced.
Embodied-BenchClaw is an autonomous multi-agent system for constructing embodied spatial intelligence benchmarks. Through a 5-stage pipeline (intent, data collection, structuring, synthesis, reporting) coordinated by 3 agents, it automatically generates reusable and maintainable benchmarks covering indoor/outdoor spatial reasoning, robotic manipulation, quadruped navigation, and aerial-view understanding.
MAD2 is a benchmark of 1,000 two-speaker audio dialogues (3,368 verifiable claims, ~10h audio) for misinformation detection in conversation. Authors propose calibrated multimodal fusion combining context-aware audio encoder and dialogue-aware text model. Conversational structure improves verification more than misinformation framing.
Novel Bayesian Optimization framework with range-aware acquisition function to discover multiple distinct designs satisfying target property windows instead of single optima. Validated on polymer synthesis and oligomer discovery with quantum chemical calculations, recovering larger and more diverse solution sets than standard BO baselines.
Perspective paper on integrating hard physical constraints into generative models for semiconductor manufacturing. Argues generative AI must be physics-informed by construction rather than corrected post-hoc. Surveys physics-informed diffusion, PDE-constrained variational models, neural-operator priors, and conservation-law-respecting networks.
StatefulDiscovery is a framework for open-ended scientific discovery that externalizes investigation state to coordinate frontier selection, evidence acquisition, and claim adjudication. Evaluated on 40 real-data tasks, it produces more well-supported and high-value claims than baselines, with structured hypotheses and frontier control driving performance.
APEX is a transformer foundation model specialized for wireless network telemetry. Pre-trained on 10-channel multivariate data from ~4,500 production networks (100K time series), it reduces MAE by 18% vs Toto and 38% vs SARIMA on 192-step DHCP forecasting. Two versions: APEX-Large (269M, cloud) and APEX-Edge (10.5M, edge) with F1=0.93 for anomaly detection.
FlowBank optimizes LLM-based multi-agent workflows through a reusable bank of complementary workflows. The three-stage framework (DiverseFlow for diversification, CuraFlow for compression, adaptive matching) improves over baselines by 4.26% to 14.92% across five benchmarks while remaining cost-competitive.
SirenFNO combines Fourier neural operators (FNO) with sinusoidal representation networks (SIREN) to learn full-spectrum without frequency truncation. The framework reduces parameters by 4–15× on PDE benchmarks while eliminating spectral bias toward low frequencies; functional tensor decomposition variants achieve 73× parameter reduction.
arXiv paper testing LSTM neural networks to detect structural breaks in property insurance loss reserves amid climate-driven catastrophes. Using 15+ years of regulatory data (Florida, Louisiana) enriched with NOAA hurricane indices, LSTMs target 15-20% accuracy improvement over Chain Ladder/Bornhuetter Ferguson methods. Probabilistic theoretical framework provided.
Anthropic reverses a secret policy that would have degraded Claude Fable/Mythos performance for AI researchers without notification. The company acknowledges making 'the wrong tradeoff' and now makes safeguards visible.
Anthropic reverses a policy that could have hindered researchers using Claude. The decision follows criticism over restrictions placed on academic users and AI researchers.
Hyperparameter search on MTP and speculative decoding with llama-server on Qwen 3.6 27B. 6% improvement (13.24 tokens/sec) via Optuna. Python script provided.
DeepSeek v4 Pro scores 80.6 on SWE-bench and 93.5 on LiveCodeBench but CAISI rates it 8 months behind US frontier (vs 2 months per DeepSeek). Coding benchmarks are narrow and heavily optimized; gaps emerge in cybersecurity and abstract reasoning. Quantized local versions drift further from headline scores.
Observer framework adds MCP for screen monitoring micro-agents with single-sentence commands. Supports gemma-4 (e2b/e4b), llama.cpp, webGPU. Free, open-source, no installation needed.
Developer builds fully offline voice loop for Ollama + LM Studio. Stack: Silero VAD (voice activity detection), Parakeet TDT 0.6B (ONNX INT8 STT, 25 languages), Supertonic TTS 3 (multilingual ONNX synthesis). CPU-only, zero data leaves machine. Cross-platform (macOS/Linux/Windows), tested on 4-year-old ThinkPad.
AI coding agents do not replace software engineers. Despite expectations, these tools remain auxiliary technologies limited by integration challenges, reliability issues, and complex context handling.
Inverse Rubric Optimization provides a testbed for agent science. The project offers infrastructure to evaluate agent behaviors in structured scenarios.
AMD promotes unified memory architecture (UMA) for next-generation processors, including Ryzen AI MAX 400 series (Gorgon Halo). This approach aims to improve local LLM performance by unifying CPU-GPU memory access.
OpenAI supports the EU Code of Practice on AI content transparency, advancing provenance standards and tools to help users identify AI-generated content.
Vercel plugin is now available in Grok Build. It dynamically injects Vercel knowledge (real-time activity, file edits, terminal commands) into context to keep answers aligned with current platform APIs and recommended patterns.
BBVA deploys ChatGPT Enterprise to 100,000 employees and partners with OpenAI to accelerate AI-powered banking transformation.
OpenAI acquires Ona to enhance Codex with secure, persistent cloud environments, enabling long-running AI agents in enterprise workflows.
Azure now supports DeepSeek V4 Pro and V4 Flash on Vercel AI Gateway. Requests automatically route through Azure with fallback to other providers—no code changes required. Supports BYOK (bring your own keys), zero platform fees on inference.
Part 2 of a PyTorch profiling guide. Detailed performance analysis of nn.Linear layers and construction of an optimized fused MLP. Demonstrates operation fusion techniques to reduce latency and improve computational efficiency.
Astrophysicist Chi-kwan Chan uses OpenAI's Codex to build black hole simulations, enabling scientists to study extreme physics and test Einstein's theory of general relativity.
datasette-agent 0.2a0 enables tools to ask users questions mid-execution via ask_user() supporting yes/no, multiple-choice, or free-text questions. Suspended conversations persist to database and survive server restarts. New built-in save_query tool lets agents save SQL queries as Datasette stored queries with mandatory human approval.
Research identifies deficits in executive control within transformer attention mechanisms. Study shows limitations in models' ability to maintain and switch between complex cognitive tasks.