2731 articles
Researchers propose graph neural networks based on Finsler geometry to overcome limitations of graph Laplacian-based architectures (isotropic operators). They prove discrete convergence to the true operator on manifolds and express this operator as a GNN layer, validating recovery of nonlinear geometries.
Empirical study of cross-lingual transfer in In-Context Learning (ICL) spanning 7 tasks, 6 models, and typologically diverse languages. Results show that fine-tuning-based expectations do not consistently apply in the ICL regime, proposing alternative heuristics for effective source language selection.
VoidPadding introduces a dedicated [VOID] token for padding in masked diffusion language models (MDLMs), freeing [EOS] for semantic termination. On Dream-7B-Instruct, it improves mathematical reasoning and code generation benchmarks by +17.84 points over baseline and +6.95 over RainbowPadding, reducing NFE by 55.7%.
MODE is an expert-level mixed-precision quantization framework for MoE multimodal LLMs. It decomposes expert selection frequency by modality (vision/text) and filters redundant vision tokens to correct estimation biases. Results: <2.9% performance loss at W3A16.
Post-training refinement method for speech foundation models using interventional contrastive learning. Transforms entangled representations into separate content and speaker subspaces via interventional dataset and multi-part contrastive loss. Improves out-of-domain speaker verification and keyword spotting performance.
GNN surrogate for CO₂ migration forecasting in complex geological formations. Model trained on SPE11A benchmark with anisotropic message-passing mechanism capturing directional transport. Produces competitive forecasts of gas saturation and liquid-phase density over extended forecasting horizons.
Fine-tuning Qwen3.5-27B to predict PHQ-9 depression scores directly from transcripts of conversations with an AI mental health application. 6,283 users (3,111 ground-truth labels + Claude Opus pseudolabels). Performance: MAE=2.6, RMSE=4.0, r=0.80, AUC=0.91 at PHQ-9≥10 clinical threshold.
Automated prompt optimization framework for LLM agents in interactive environments. Decomposes observation-to-action pipeline into descriptor and action-selection agents, iteratively refines via LLM-driven evolutionary loop guided by environment returns. On BabyAI/BALROG: improves from 0% to 72.5% success on PutNext without fine-tuning.
GameCraft-Bench evaluates coding agents' ability to generate playable games end-to-end in Godot. The benchmark comprises 140 tasks across 15 game families. Top agents achieve only 41.46% success, revealing struggles to produce complete games with sufficient content and coherent visual feedback.
RL-trained reasoning models often generate unnecessary reasoning after finding the correct answer (overthinking). This paper introduces Dynamic Rollout Editing (DRE), a training-time intervention during GRPO that edits successful trajectories continuing after answer emergence, preserving the verified prefix and weakening preference signals for unnecessary thinking.
DiagFlowBench evaluates how language models handle off-procedure inputs in industrial diagnostic dialogue. A dataset of 1,676 multi-turn conversations derived from 50 diagnostic flowcharts reveals models often select a real but contextually inadequate step rather than hallucinate, exposing a vulnerability: plausible but wrong advice grounded in documentation.
ChLogic is an English-Chinese aligned benchmark evaluating the robustness of logical reasoning in LLMs. Built from formal logical templates, it contains 100 aligned propositions and 15 Chinese-specific phenomena. Experiments on Qwen3, Ministral, and GLM reveal a persistent English-Chinese performance gap, with back-translation producing mixed effects.
Study on bilingual fine-tuning for low-resource ASR across 9 language pairs. Uses language identification tokens prepended to input text. Results: bilingual fine-tuning improves performance when language ID accuracy is high; providing the token at inference mitigates low language ID performance.
Comparative study of LLM abilities to predict next speaker, turn changes, and addressee in multi-party conversations. On the AMI corpus, LLMs outperform supervised models and humans in next speaker prediction without audio-visual access. MM-LLMs exceed text-based LLMs but remain below human performance for addressee and turn-change prediction.
Pruned models pass multiple-choice benchmarks but fail in open generation. Multilingual study shows that under high-sparsity pruning (Wanda), correct answers are demoted rather than erased: they reappear with beam search or sampling. Multiple-choice benchmarks overstate the usability of compressed LLMs.
OPD-Evolver is a slow-fast co-evolution framework that cultivates self-evolving agents through on-policy self-distillation. The system manages a four-level memory hierarchy to read, use, write, and maintain experience. Across multi-domain benchmarks, OPD-Evolver outperforms ReasoningBank (+11.5%) and Skill0 (+5.8%), with OPD-Evolver-9B rivaling Qwen3.5-397B and Step-3.5-Flash.
Study on agent routing at scale: with 110 agents and 584 tools, F1 accuracy drops 16–23 percentage points on under-specified requests. Analysis decomposes degradation into retrieval gap and confusion gap (10pp oracle ceiling loss). Embedding-based shortlisting recovers +10–11pp F1 at full scale across models.
ProCUA-SFT is a dataset of 3.1M step-level SFT samples generated automatically from 93K synthetic trajectories across 2,484 application combinations. Fine-tuning UI-TARS 7B on ProCUA-SFT achieves 45.0% on OSWorld, a +18.7 percentage-point improvement over the base model and +35% above AgentNet. The pipeline uses Kimi-K2.5 as task generator, precondition judge, and trajectory executor.
STATEWITNESS, an activation explainer, detects deception in reasoning LLMs by reading the target model's hidden states and answering natural-language queries. Achieves 0.916 mean AUROC, 11.6% relative gain over best black-box text monitor, 25.0% over best activation-probe baseline. Provides token- and sentence-level evidence traces for human inspection.
InferBERT combines transformers with Do-calculus to detect causal adverse drug events in pharmacovigilance. Comparative study on AILF and TRAM benchmarks: BioBERT outperforms XGBoost, ALBERT, and Med-LLaMA. Finding: domain-specific pre-training outweighs model size.
Theoretical analysis of deep transformer expressiveness through bounded-depth, non-recursive context-free grammars. Authors explicitly construct transformers with positional attention whose depth scales linearly with grammar depth, demonstrating these architectures can encode abstract grammatical states into linearly separable subspaces within the residual stream.
LLM-as-Environment-Engineer framework: the policy model analyzes failure trajectories and proposes modifications to the next-stage RL training environment configuration. MAPF-FrozenLake testbed with multi-dimensional configurations. Qwen3-4B outperforms GPT and Gemini on proposed benchmarks.
Study of second-order bias in LLMs: how models judge biased content, beyond generation. Grounded in entitlement epistemology, the method evaluates whether LLMs infer demographics without sufficient support. Findings: systematic bias across target groups, evasion of safety guardrails, persistence of demographic triggers.
arXiv paper on critic-free reinforcement learning for LLMs. Authors challenge the role of rollout groups in existing methods and propose negative token filtering to enable stable single-rollout training, improving performance on agentic tasks compared to group-based RL techniques.
PowerOPD stabilizes on-policy distillation for LLMs by replacing unbounded log-ratio rewards with Box-Cox power transformation. On 6 mathematical reasoning benchmarks with Qwen3, achieves +6.37 Avg@8/+5.71 Pass@8 gains vs vanilla OPD, reduces wall-clock time by 59.2% and peak GPU memory by 23.1%.
Generative model based on GPT architecture for inverse design of heterogeneous catalysts. Pretrained on 133 million structures, fine-tuned on ~460,000 optimized structures. Achieves 98% structural validity, 95% optimization validity, and improves screening efficiency 1.5–4× for reaction-targeted catalyst discovery.
Hardware-aware finetuning method for DNN deployment on ReRAM crossbar arrays. Uses range-shrunk sinh transformation to mitigate I-V non-linearity and incorporates retention errors into regularization loss. Results: ResNet18/DeiT-Tiny no degradation, MobileNetV3 <2% on ImageNet, F-1 -1 point on SQuAD v2.
Study of 450 chest X-ray reports showing LLM rewriting for standardization preserves image-text alignment (2.5% degradation) but erodes 26.8–29.3% of clinical entities and 14.9–16.5% of uncertainty language. The paradox: tasks producing 'cleaner' text pull content away from images.
AIPatient Arena evaluates LLMs in multi-turn clinical consultation across 8 competence dimensions using EHR-grounded knowledge graphs. On 437 patients, models excel in questioning (4.43-4.99/5) and ethical conduct (4.38-4.93/5), but fail in diagnostic accuracy (2.63-3.55/5) and information coverage (2.08-3.02/5). Weaknesses include repetitive questioning, omitted medical history, inadequate uncertainty handling.
PreAct compiles successful runs of computer-using agents into small state-machine programs, replayed 8.5-13x faster with no per-step LLM calls. An independent evaluator validates each program before storage. Across three benchmarks (mobile, desktop, web), this verification prevents faulty program accumulation (+1.75-2.6 tasks).
Vercel Connect, now in Public Beta, replaces long-lived stored tokens with runtime credential exchange. Agents receive short-lived, task-scoped credentials through reusable connectors (Slack, GitHub, etc.), eliminating risks from permanent token leaks.
Researchers identify an internal signature of deception in language models: deceptive responses show 2.1-2.3x higher residual rank than naively false answers. This signature detects deception with 100% accuracy on GPT-2, Qwen2.5, and Phi-3, and transfers zero-shot across model families and languages (AUC 0.933-1.0).
MM++ is an unsupervised, post-hoc method for out-of-distribution detection. It fuses intermediate layers selected by entropy density with the final representation using Ledoit-Wolf regularized covariance, requiring no auxiliary OOD data, fine-tuning, or architectural changes.
Study of LLM verbalized confidence reliability in machine translation. Five methods for extracting per-token confidence without internal signal access are compared against predicted probabilities. Results: similar performance for error detection and calibration, but little correlation between internal and verbalized methods.
MLLP-VRAIN group participates in IWSLT 2026 simultaneous speech translation using Parakeet and Qwen 3.5 models. Cascaded system with adaptive policies and RAG mechanism for domain-specific context. +5.82 XCOMET-XL improvement on En→De test set versus previous year.
LLM-based ASR systems often misidentify output languages in multilingual contexts. Authors propose three mitigation strategies: zero-shot prompting, supervised fine-tuning, and Chain-of-Thought reasoning to improve language adherence while preserving code-switching flexibility and ASR performance.
Discrete autoregressive transformer for mechanism synthesis. Conditional sequence model with VAE latent and quantized joint coordinates. Trained on >1M mechanisms with Chamfer distance and DTW metrics. Mean Chamfer distance 0.0132, DTW 0.153 on held-out tests.
Deep learning framework for retrieving atmospheric CO2 from NASA's OCO-2 satellite spectra. Uses Laplace approximations and normalizing flows for uncertainty quantification. Inference orders of magnitude faster than operational algorithms, with better-calibrated non-Gaussian posterior estimates.
Theoretical paper on Sum-of-Squares degree barriers for robust halfspace learning under malicious noise. The Christoffel function exactly characterizes corruption hidden from bounded-degree certificates. Proves a margin-degree tradeoff and a degree-2t algorithm achieving the frontier η^(1-1/2t).
Adaptive sequential sampling method for polynomial chaos expansion surrogate models, generalized for multiple quantities of interest. The approach balances input space exploration with exploitation of aggregated variance across outputs, improving surrogate accuracy and stability compared to Latin Hypercube Sampling.
Study on language models' ability to discover the mathematical concept of zero. GPT-2-sized models fail without additional training, but improve substantially after exposure to tens or hundreds of examples. Language pretraining reduces required examples by ~50%.
Analysis of 4,434 posts and 50,338 comments on Moltbook showing parasocial interaction cues (intimacy language, reciprocity bids, self-identification) persist in autonomous AI-agent communities. Results validated through keyword matching and LLM annotation reveal strong association between these signals and original poster re-engagement and sustained dyadic patterns.
SpeechDx is a multi-task benchmark for clinical speech AI covering 12 datasets and 27 tasks across diverse health conditions. Tasks are structured by speech production stages (conceptualization, formulation, articulation). Evaluation of 12 audio encoders shows large-scale speech models outperform domain-specific ones, but none generalize reliably across clinical speech.
CheckMIABench introduces a benchmark for principled evaluation of membership inference attacks (MIAs) on language models. Leveraging intermediate checkpoints from open-source models (Pythia, OLMo, 70M–7B parameters), the authors construct reliable testbeds where training data before and after a fixed point share the same distribution. They evaluate six published attacks and release a modular library (pandora_llm).
Training method to improve token editing in diffusion language models (LLaDA2.1). Addresses training-inference mismatch between random corruptions and model's own errors. Uses no-gradient draft pass followed by supervision on self-generated corruptions via LoRA. Reduces edit intensity and transcription errors.
7B model fine-tuned to predict next step in concurrent Go programs by learning event distributions rather than single labels. On 798 predictions from real bugs (CockroachDB, Kubernetes, gRPC, etcd), achieves 36.2% accuracy with <1000 traces, outperforming Gemini 3.5 Flash zero-shot (34.8%). Dataset, adapters, and tooling released.
MapSatisfyBench is a benchmark for evaluating LLM agents integrated into map services. It measures their ability to identify and satisfy implicit user needs (unspoken decision factors) from real-world behavioral data. Experiments show current agents perform well on explicit task completion but struggle to proactively address implicit factors.
FllumaOne is a multimodal CAD dataset of 100,000 models generated by executable Python programs in Flluma (OpenCASCADE-based CAD system). Each sample aligns the program with a feature tree, STEP representation, point cloud, and natural-language descriptions. A Qwen2.5-Coder-1.5B baseline achieves 99.98% Python syntax validity and 99.14% STEP-export validity.
REEF-GP, a post-hoc uncertainty quantification framework for neural operators, adapts the operator's intrinsic representations to construct geometry-aware uncertainties. Tested on 5 PDE benchmarks, it preserves predictive accuracy while providing calibrated uncertainty estimates, more efficient than deep ensembles.
Study of LLM adaptation for 3D CT report generation in medical imaging. RAD3D-Prefix, a lightweight diagnostic-prior framework, integrates image embeddings and multi-label classification logits. Across LLMs from 96.1M to 1.6B parameters, freezing the model and training only projection layers outperforms full fine-tuning, reducing clinical hallucination and overfitting.
MODE-RAG is a multi-agent system driven by Variational Free Energy to reduce hallucinations in Multimodal Retrieval-Augmented Generation. It uses Monte Carlo Tree Search, logit perturbations, and specialized agents to route high-risk queries and perform post-hoc factual verification. Authors introduce ModeVent, a challenging subset of MultiVent dataset, to evaluate M-RAG robustness.
Quantized integer-only transformer implementation for jet tagging on AMD Versal AI Engine (AIE). Reusable software framework automatically converts Python model descriptions to Vitis graph code for low-latency, resource-constrained deployment. Open-source release.
Online learning algorithm for dynamic LLM selection in edge-cloud systems under budget constraints (cost, latency). Formulated as constrained stochastic bandit with time-varying demand. Theoretical guarantees: sublinear regret and sublinear constraint violations.
Multi-Adapter PPO framework for wavelength selection in LIBS quantitative analysis. Uses RL with cross-attention mechanisms and specialized adapters. Outperforms PSO by 28.4% in comprehensive score and 45.2% in prediction accuracy on steel and coal datasets. Code and dataset released.
MultiClin, a clinical ASR benchmark, evaluates speech recognition model robustness to multiscript variability (multiple valid orthographic forms of the same term). Conventional metrics underestimate performance. Script unification consistently yields best ASR performance.
CEO-Bench, a multi-agent benchmark, evaluates LLMs' ability to make strategic resource reallocation decisions. Five frontier models tested on 13 scenarios show high structural validity but diverge on strategic calibration. Failure modes include single-advisor capture and historical amnesia.
PromptMN is a domain-specific language that structures natural prompts with %-prefixed typed directives (roles, goals, constraints, outputs). Tested on Claude Opus 4.8, Gemini 3.1 Pro, and GPT-5.5 without fine-tuning, it reduces context ambiguities in agent and software development workflows.
MemSlides introduces a hierarchical memory framework for personalized presentation agents. It separates long-term memory (user profiles, tool experience) from working memory (active preferences), enabling multi-turn local revisions without full deck regeneration.
FinAcumen is a financial multimodal reasoning agent that accumulates experience from prior trajectories in persistent memory. The system improves a frozen 8B vision-language model across four financial benchmarks using selective experience activation and a deterministic tool environment for numerical computation and verification.
MathVis-Fine introduces a framework for fine-grained visual dependency modeling in mathematical reasoning. A new dataset augments visual annotations with visual dependency ratings. Two-stage progressive training balances answer correctness and visual grounding rewards according to each sample's intrinsic visual necessity, reducing reward bias.
Researchers propose Equation-to-Behavior Prompting to guide LLMs to simulate diverse cognitive models (Bayesian, motivated reasoning, Grether's α-β model). Large models approximate these specifications via prompting, but small models fail. RL training reduces belief error by 26.5% and improves performance by 2.5–12% on legal persuasion games.
Comparative study of narrative diversity across 10 LLMs versus human authors using r/WritingPrompts dataset. Models generate stories significantly more similar to each other than human-written texts, converging toward a generic mean narrative. Temperature scaling and negative prompting fail to address this homogeneity.
SEAGym is an evaluation environment for measuring self-evolving LLM agent harness updates (prompts, memory, tools, interaction loop). The study compares ACE, TF-GRPO, and AHE on Terminal-Bench 2.0 and HLE, showing frequent updates don't guarantee held-out performance gains and source diversity affects harness reliability.
E³RL, a reinforcement learning method, addresses error propagation in long-horizon reasoning of LLMs. Using autoregressive cross-entropy as an epistemic uncertainty signal, the model can locally correct logical defects and reuse KV cache. On AIME, 4B and 8B models outperform SOTA by 5.349% and 6.514%.
Machine-Learned Comorbidity Index (MLCI) maps diagnosis codes to a single scalar by maximizing normalized Hilbert-Schmidt Independence Criterion across multiple clinical outcomes. Unlike traditional indices (Charlson, Elixhauser), MLCI captures nonlinear risk-outcome relationships and outperforms baselines on multiple EHR datasets.
Study of harness-model alignment via 138k agent trajectories. Authors introduce Simple Strands Agent (SSA), a generic harness tested on Claude, Gemini, GPT, Grok, Qwen across SWE-Pro, SWE-Verified, and Terminal-Bench-2. Beyond pass@1 scores, analysis reveals fine-grained behavioral differences: edit frequency, testing activity, phase transitions.
Word2Vec study on Toki Pona, constructed language with ~130 words. Training on 1.4M sentences (7.95M tokens). Comparison of two models: with and without non-Toki Pona tokens (named entities, loanwords). Finding: sparse tokens bring similar words closer; Word2Vec works even with extremely reduced vocabulary, relying on distributional patterns rather than lexicon size.
SkillMigrator is an LLM agent that learns reusable web skills and transfers them across sites by matching layout structure rather than specific element references. Induced skills are stored as transferable interaction patterns (TIPs). On WebArena and Mind2Web, SkillMigrator reduces average LLM-action count by 8-10% at matched success rate.
Theoretical paper generalizing neurosymbolic systems using homotopy type theory. The framework preserves symmetry and multiple-proof information, converting classical functionals into belief-weighted homotopy cardinalities. Validated on MNIST reasoning-shortcut benchmarks with better calibration than diversity-trained ensembles.
Curriculum-grounded automated marking pipeline using LLMs to assess exam responses. Grounds model outputs in official curriculum artefacts (syllabus, performance descriptors, marking guidelines). Delivers marking outcomes comparable to human tutors with improved traceability to authorised standards.
Researchers explain grokking (sudden generalization after prolonged overfitting) through first-order phase transitions driven by L2 regularization strength. SGD noise enables networks to escape trapped metastable states, with escape times following Arrhenius scaling. Results extend to nonlinear networks.
Sequential decision optimization framework for geosteering under geological uncertainty. Integrates particle filtering for probabilistic subsurface interpretation with value-based reinforcement learning. Compares three decision policies: Approximate Dynamic Programming, Deep Q-learning, and Dual DRL with dueling decomposition, validated on industrial simulator with realistic noise and drilling constraints.
Two recent studies reach contradictory conclusions about LVLMs' ability to coordinate efficient referring expressions. This research controls for task differences and directly compares prompting styles. Models coordinate efficiently with explicit prompting but fail to infer communicative efficiency needs from implicit prompts.
LLMs can infer cultural context but fail to apply it in responses. A new CAPRI dataset shows models recognize cultural conventions (measurement units, time interpretation) but don't spontaneously use them unless explicitly instructed. Biases remain aligned with the model's country of origin.
LongWebBench is a benchmark evaluating long-horizon webpage generation by vision-language models. It contains 490 real-world pages for structural evaluation and 507 goal-oriented interaction tasks over 129 pages. Experiments show structural fidelity degrades with webpage length, and visually plausible generations often fail to support multi-step executable interactions.
DivInit improves test-time scaling for agentic search by diversifying initial queries. Instead of sampling k independent queries in parallel, the method generates n candidates then selects k diverse seeds. Gains of 5-7 points on multi-hop QA at matched compute, validated across 5 open-weight models and 8 benchmarks.
Theoretical analysis of generalization guarantees for multi-input neural operators with error measured in Sobolev norms. Framework handles multiple input functions on different domains with varying dimensions and regularities. Approximation and generalization rates explicitly quantify each input space's contribution to the final error bound.
DeepInsight is a unified evaluation infrastructure for Physical AI stacks, spanning three orders of magnitude from foundation-model decoding to full-body physics simulation. It uses three invariant abstractions (task, resource, result) to preserve regime heterogeneity while enabling cross-layer regression diagnostics impossible with federated per-segment harnesses.
Diffusion-based approach to generate irregular clinical time series by jointly modeling laboratory values and observation patterns. Uses DACMI benchmark from MIMIC-III, extends TimeDiff framework to capture dependencies between patient physiology and clinician testing behavior under MNAR-like missingness.
MemTrace is a benchmark evaluating long-term memory in LLM agents across three dimensions: memory age, question type (current state, earlier state, trajectory), and evidence conditions. Testing 13 configurations, the study finds that evidence use is the primary bottleneck (10× more often retrievable than missing), not retrieval itself.
Vercel introduces eve, an open-source agent framework for building and deploying agents in production. eve provides built-in infrastructure (model management, fallbacks, logging); developers define only behavior through files (agent.ts, instructions.md, tools). Inspired by Next.js for the web, eve standardizes agent building as Next.js did for web applications.
Brick-DICL introduces a two-stage dynamic in-context learning framework for automated Brick schema classification of BMS points (936 classes). Combines metadata-RAG and class-RAG to enhance LLM domain knowledge, with multi-LLM filtering to reduce manual verification effort.
arXiv paper proposing architecture for distributed peer-to-peer autonomous agent networks. Authors identify three core mechanisms: semantic announcement propagation for collaborator discovery, verifiable identity and multi-topic reputation (MG-EigenTrust), and mechanism design for open task execution. Prototypes and simulations presented.
StepGuard improves web navigation for AI agents via Dynamic Dual-Policy Optimization (DDPO) to handle reward conflicts and Confidence-Guided Adaptive Navigation Reflection (CANR) to calibrate per-step errors. The framework achieves state-of-the-art performance on standard web navigation benchmarks.
Study evaluating 12 frontier models on inference compute impact across seven benchmarks. Three interventions tested: larger token budgets, context compaction, repeated submission attempts. Results: increased budgets substantially improve performance on FrontierMath, Humanity's Last Exam, TerminalBench. Fixed-budget evaluations increasingly understate newer model capabilities.
Web Component <click-to-play> that converts a static image into a play button to load GIFs on demand. Improves performance by preventing automatic loading of large files.
Snap launches its consumer AR glasses. The article questions actual product adoption amid competition and social acceptance challenges for users.
Analysis of low narrative diversity in LLM-generated stories. The author examines why models produce repetitive tales with similar characters and structures despite varied prompts.
Leaked OpenAI financial documents show a $38.5B loss and significant compute burn. The figures raise questions about the economic viability of large-scale model training.
Benchmarks of modded AMD Radeon Pro W6800 (V620 with W6800 firmware) tested with Qwen 3.6 27B Q6_K on llama.cpp. Vulkan performance: 297.94 t/s (pp1024), 20.35 t/s (tg256). Firmware enables mini-displayport but disables some compute cores.
France abandons Palantir's AI data tools in favor of a domestic provider. Political decision to assert technological sovereignty against US solutions.
Vercel releases eve, an open-source framework for building and deploying AI agents. Minimal agent requires only two files (model + instructions). Add tools, skills, channels by creating files. Deploy to production with vercel deploy, unchanged from local development.
OpenAI introduces LifeSciBench, an expert-authored and expert-reviewed benchmark for evaluating AI systems on real-world life science research tasks and decisions.
Hugging Face introduces agentic resource discovery, enabling AI agents to autonomously search and access models, datasets, and tools available on the platform. This capability enhances agent autonomy in executing complex tasks.
Vercel removes CLI-specific deployment limits, enabling faster deployments from local machines and external CI/CD pipelines. Teams and AI agents can now deploy at the pace their workflows demand.
Vercel Passport, access control tool for deployments, enters public beta. Centralizes authentication via Okta, Auth0, or OIDC providers. Pricing: $100/project/month, unlimited external users.
VibeThinker-3B, a 3B model, achieves exceptional MathQA results comparable to ~30B models. Reddit users report abnormally high performance for its size.
Vercel launches Enterprise Apps and Agents platform to safely deploy internal AI agents. Vercel Passport authenticates access via identity providers (Okta, Entra, Auth0), while a credential management solution consolidates OAuth, OIDC, and secret injection.
User compiles llama.cpp with CUDA and Vulkan enabled simultaneously on W7800. Achieves +10% tokens/sec improvement in decoding with MiniMax-M3-UD-IQ2_M. Tests dual GPU accelerator combination for performance optimization.
Datasette 1.0a34 adds tools to insert, edit and delete rows directly in the web interface. These long-overdue features are available on table and row pages, inspired by Datasette Agent which now supports SQL write operations.