Anubis OSS v3.6, macOS app for benchmarking local LLMs (Ollama, LM Studio, MLX), adds direct model downloads from UI. Available via Homebrew and direct download. Call for testing on Apple Silicon. GPL-3.0, open-source, leaderboard with 400+ runs.
Firecrawl now available on Vercel Marketplace. Vercel teams can power AI agents and applications with structured web data without managing crawling infrastructure. Key features: scrape pages to markdown/HTML/structured data, search and retrieve full page content, interact with dynamic websites via AI prompts.
A lawyer shares experience running a 12 V100-SXM2 32GB cluster for local legal document drafting. After abandoning vLLM due to GPU Volta incompatibility with MoE models, he switched to llama.cpp with Gemma-4-26B and Qwen3.5-122B. Dense models on V100 are inefficient (~20-28 tok/s); MoE models achieve 50-113 tok/s decode on long-context legal prompts.
Fine-tuned Qwen 3.5 0.8B on Pangram's EditLens dataset to detect AI-generated content. Chrome extension 'Slop Hammer' released for local inference (~1s on M1), 400MB model. 20h training on single RTX 3090. Limitation: dataset built with older LLMs, struggles with GPT-5.5.
Developer builds sandbox framework for AI agents on headless Linux with GPU passthrough, sudo access, and host OS isolation. VM-based architecture enables autonomous web browsing, Docker execution, and parallel sessions. Code released on GitHub.
Mininglamp AI added W8A8 activation quantization to MLX via Cider, a custom SDK with Metal kernels. On M5 Pro, prefill improved from 2.84s to 2.52s for a 4B VLM. Works with any MLX model, but INT8 TensorOps requires M5+.
Leading AI models like GPT and Gemini routinely cite text passages that don't support their answers, even when answers are correct. Researchers at Peking University term this "attribution hallucination" and introduce CiteVQA benchmark to systematically test for it.
Qwen 3.6 benchmarks on 2x RTX PRO 6000 with vLLM. Qwen 3.6 27B BF16 reaches 1800 tps (64 concurrency, MTP-2). Qwen 3.6 35B BF16 reaches 3500 tps generation (128 concurrency, MTP-Off) with 30k tps prompt processing.
Developer builds custom C++ inference engine for MiniCPM-V 4.6 on Orange Pi AIPro (Ascend 310B NPU, $149). Bypasses heavy frameworks with optimized AscendC kernels, achieving 5.90 tokens/s vs 2.88 baseline (170ms per step). Open-source on GitHub.
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.
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.
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.
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.
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.
VDSS, a multi-agent system for ventilator decision support, coordinates modular components through structured interfaces and contextual bandit preference learning from clinician feedback. Structured rejection triggers targeted replanning. Retrospective ICU validation shows higher recommendation acceptability and fewer interaction cycles.
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.
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.
Theoretical paper proving architecture alone sets an accuracy ceiling (Deterministic Horizon) between 19–31 layers across 12 transformers. Beyond it, no training improves it. Converts 16 impossibility results (Turing, Arrow, No Free Lunch) into design rules for trustworthy AI systems, with computable bounds and quantified violation costs.
Theoretical paper on mediative fuzzy logic: unified formalization of type-1 foundations with type-2, type-3, and quantum extensions. Characterizes the mediative operator as convex aggregation controlled by hesitation and contradiction. Establishes semantic coherence and demonstrates application to autonomous-braking sensor fusion.
ModeSwitch-LLM is a lightweight controller routing each request to an optimal inference mode (FP16, quantization, speculative decoding, GPTQ+prefix caching, INT8+continuous batching) on single GPU. Tested on Llama-3.1-8B on A100, it achieves 2.10x latency speedup and 51.7% energy reduction per token while preserving accuracy (+0.17pp vs FP16).
NED3 Laboratory releases an open-source ecosystem of multimodal datasets and software for data-driven modeling of multiphase transport and thermal systems. S+TD framework classifies datasets from 0+0D to 3+0D dimensionality; 7 software packages (BubbleID, SeqReg, CFDTwin, IRISApp, decode-wfs, AELab, FlowLab) cover computer vision, sequence regression, and multimodal diagnostics.
RADAR is a geometric metric for estimating cross-domain transferability in foundation models. It analyzes layer-wise representation evolution by measuring angular alignments and distance changes. Evaluated on cross-lingual sentiment classification and cross-domain image classification, RADAR achieves competitive performance against existing transferability metrics.
FederatedRSF is a Python package implementing federated random survival forests for multi-center survival prediction without sharing raw patient data. The system handles feature-space heterogeneity (different covariates across sites) by redistributing only compatible trees. Evaluation on GBSG2 breast cancer cohort shows performance comparable to centralized training.
HCLM theoretical framework for entropy-regularized learning in open dynamical systems. Introduces effective information force concept and characterizes degenerate entropy regimes. Shows geometric entropy surrogates (variance, log-determinant covariance) produce stronger, more stable gradients than softmax-normalized entropy.
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.
WeCon is a neural solver for Multi-Objective Combinatorial Optimization Problems (MOCOPs). It introduces Gated Residual Fusion blocks to better integrate weights and features, a Residual Fusion block in the decoder, and an Efficient Preference Optimization method. On 4 MOCOP variants, WeCon matches POCCO-W's HyperVolume while reducing inference time by 40%.
ManiF-SMC proposes machine unlearning via manifold representation forgetting with adaptive margin-based triplet loss guided by self-mode connectivity. The method pushes erased samples away from original learned manifold centroids toward retained data neighbors, operating purely in representation space. Experiments on 4 datasets match state-of-the-art approximate unlearning effectiveness.
Latent Cache Flow (LCF) enables direct model-to-model communication via compressed latent representations instead of text. The approach reduces adapter size to 4% of Cache-to-Cache (C2C) and handles differing contexts by transmitting a summary of new information. LCF achieves 23% higher accuracy and 8.5x faster than text-based communication.
PARPO framework for personalized agentic reinforcement learning. Decouples generic task rewards from user-specific preference rewards using user anchors. Introduces PSGM for preference-aligned skill retrieval. Evaluated on ETAPP, ETAPP-Hard, SJAgent with improvements over memory and RL baselines.
New paper introducing Emotion Recognition in Conversation (ERC) task for sign language video analysis. The eJSL Dialog dataset contains 1,920 video samples organized into 480 unique dialogues. Benchmarking reveals a domain gap when applying generic multimodal conversational models to sign language, highlighting the need for sign language-specific visual extractors.
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.
AraHopeCorpus is the first annotated dataset of Arabic hope speech from 10,000 YouTube comments on the Gaza war (2023-2024). 64% contain hope speech (religious encouragement, collective solidarity, optimism). Cohen's Kappa=0.71. ChatGPT shows limitations on dialects and culturally embedded expressions.
Method to improve in-context learning by optimizing continuous embeddings of few-shot prompts at test time. Uses model log-probabilities on demonstrated outputs as self-supervised signal, requiring no token generation or fine-tuning. Improves performance on both classification and free-form generation tasks.
DFKI-MLT applies activation steering to multilingual LLMs to improve cultural awareness in SemEval-2026 Task 7. The method adds language-specific steering vectors to the residual stream without parameter updates. Result: 86.96% accuracy on MCQ track (7th/17), but modest and heterogeneous improvements varying by language-region pair and layer selection.
Inventory of text and speech resources for Hausa (80-100M speakers) and Fongbe (2M speakers). Hausa has diverse parallel corpora and text collections (news, encyclopedic, educational). Fongbe lacks text data but benefits from recent speech collection initiatives. Both languages represented in Masakhane benchmarks (NER, POS tagging).
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.
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.
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.
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.
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.