Method to establish correspondences between embedding vectors from different black-box encoders. Exploits local geometric consistency of independently trained contrastive encoders: short-range distances preserved up to scale factor. Uses iterative reference-based geometric embedding hashing with paired anchors to recover vector links. Code released.
Novel transformer-based architecture for autonomous resource management in heterogeneous satellite clusters (optical and SAR). Uses model-free reinforcement learning for real-time decision-making in Earth Observation missions. Demonstrates significant performance improvements and transferability across varying cluster sizes.
Persona-based evaluation framework for pluralistic alignment in generative AI. Replaces monolithic benchmarks with structured manifold of synthetic cognitive profiles representing diverse human perspectives. Reveals systematic degradation of persona coherence under sequential inference, suggesting need for dynamic regulatory mechanisms.
COMPASS is a safety alignment framework for multi-step LLM search agents. It combines Cognitive Tree Exploration (CTE) to synthesize stealthy attack trajectories and Introspective Step-wise Alignment (ISA) to supervise risky intermediate actions. Results: favorable safety-utility trade-off requiring substantially less training data.
Multi-agent LLM systems assume agreement between agents indicates reliability. Authors show communication induces correlated failures and false consensus. They propose CAGE-CAL, a counterfactual agent-graph calibration framework comparing post-communication dependencies with no-communication scenarios to adjust confidence accordingly.
Method to detect and classify dataset usage in research literature using a multitask GLiNER framework. Combines dataset mention extraction, relation identification, and usage-context classification. Leverages synthetic data generation and LLM-based revalidation to address label scarcity.
Comparative study of zero-shot multi-label topic classification using knowledge graphs extracted from documents. Framework tested on 15 LLMs and 8 datasets: keyword-enhanced variant outperforms baseline, graph augmentation helps small models but hurts large ones, and self-consistency decoding increases costs fivefold without performance gains.
Study applying MAP-Elites (quality diversity algorithm) to procedural generation of FPS levels. Two novel representations (Point-Line, Spatial-Layout) improve map characterization. Topological and emergent metrics defined. MESB generates map populations with higher diversity and quality than previous approaches.
Developer built an MCP server enabling LLMs to explore the Mandelbrot set with rendering tools, presets for interesting regions (Seahorse Valley, Elephant Valley), inspection tools for iteration counts and viewport settings, color palette selection, and HTML gallery generation. Tested with Qwen 3.6-35B. GitHub: openmandel.
Datasette 1.0a32 fixes a bug with INSERT ... RETURNING queries via the new /db/-/execute-write endpoint and multiple base_url issues found during Service Worker experiments.
Benchmark on Radeon 7900 XTX: Qwen3.6-35B vs Gemma4-26B with reasoning enabled. Qwen generates 2x more tokens (14,811 vs 7,386) but Gemma is ~20% faster end-to-end (95.6s vs 118.8s). Qwen's MTP reaches 130 tok/s vs 78 tok/s, but token count becomes the bottleneck. Quality close, interesting per-task splits.
mlx-Chronos is an open-source CLI tool and community leaderboard to compare MLX inference engines on Apple Silicon (oMLX, Rapid-MLX, mlx-lm, Ollama). Measures TTFT, throughput, RAM, and thermal state with standardized methodology. Leaderboard currently populated by M2 8GB, seeking M3/M4 results.
Developer open-sources AI accelerator on FPGA (AWS F2) based on RocketChip/RISC-V with attention mechanism built into silicon. Benchmarks: 225× speedup vanilla attention, 96× TinyBERT, 50× ViT, 30× GPT-2 prefill. Native BF16 support.
Kaikaku.AI releases Epicure, three AI models separating ingredients by recipe compatibility or chemical similarity. Trained on 4.14 million multilingual recipes and FlavorDB, they generate different recommendations per source. The chemistry-only model outperforms recipe-based variants on taste and nutrition classification without direct data.
llama.cpp benchmark comparing Windows 11 and Linux (Ubuntu 26.04) on Nvidia GPU (RTX 5080 + 2× RTX 5060 Ti). No significant performance difference: Qwen 3.5 122B achieves PP 300/TG 28 (Windows) vs PP 290/TG 28.5 (Linux); Qwen 3.5 397B: PP 140/TG 16 vs PP 150/TG 15.2. Tests repeated 4 times with recent llama.cpp including VRAM optimization.
An Anthropic study finds researchers with typically male names use AI coding agents more than twice as often as those with typically female names, controlling for discipline and career level. Economists lead at 39%, education researchers at 4%. The gender gap for coding agents far exceeds that for general AI use.
mlx-Chronos is an open-source CLI tool and community leaderboard to benchmark local LLM inference engines on Apple Silicon (oMLX, Rapid-MLX, mlx-lm, Ollama). Measures TTFT, throughput, RAM, and thermal state with standardized methodology. Currently populated only with M2 8GB results.
AI search agents like GPT-5.4 and Kimi K2.6 mostly confirm their training knowledge rather than genuinely researching the web. Researchers at Harbin Institute of Technology demonstrated this using LiveBrowseComp, a benchmark based on events from the last 90 days. Without relying on training memory, performance collapses.
TCO analysis of a $6.4k local LLM server with 4x MI100 32GB GPUs and EPYC 48-core CPU. Runs 4 llama.cpp instances with Qwen 3.6 27B on ROCm. Processes 20.4M input tokens and 1.32M output tokens daily. Equivalent API cost: $3,701/year ($308/month). Author emphasizes proper hardware depreciation accounting for realistic TCO.
Simon Willison used Claude Opus 4.8 via Claude Code to implement running Python ASGI apps in the browser via Pyodide and Service Workers. This approach replaces the previous Web Workers implementation, enabling JavaScript execution and fixing Datasette Lite limitations. Working demos are available.
Attackers exploit ChatGPT and Claude's chat-sharing features to distribute malware. Fake chats mimic error messages or installation guides and bypass security tools by being hosted on trusted domains.
Vidai Community, open-source Rust binary, unifies cost attribution, guardrails and multi-provider routing for LLM calls. One-line integration by changing base_url (OpenAI/Anthropic/Google). Per-user/team/model cost tracking, hard budgets, 1.95ms median overhead, 21,803 RPS on single node.
Developer built NeuralDBG, a PyTorch debugger that automatically detects training failures (vanishing/exploding gradients, data anomalies). Key insight: failures are layer-localized, not global. Effective monitoring: gradient norm transitions per layer rather than raw histograms. Open-source tool available on PyPI.
Shadow AI is an open-source (AGPL-3.0) local voice assistant for Windows. Natural multilingual conversations, local web search via SearXNG, persistent memory, optional Google integrations (Gmail, Calendar, Drive). Uses user's free Gemini API key, zero remote servers.
User benchmarks Flash Attention 2 (ai-bond) on V100. Results show 7-24x speedup in backward pass, memory reduction up to 91.9% (323.4 MB saved). Thinking time before answering minimized. Numerical validation passes on causal and non-causal configurations.
MTP (Multi-Token Prediction) benchmark on Gemma 4 31B and Qwen 3.6 27B using vLLM and llama.cpp. Result: 3.34x speedup (132.52 vs 39.69 tok/s). vLLM outperforms llama.cpp on Gemma 4; llama.cpp solid on Qwen. No confirmed quality degradation, VRAM overhead negligible.
OpenAI is offering its life sciences AI model GPT-Rosalind for free through the Rosalind Biodefense program to help governments prepare for future pandemics. Early partners include Lawrence Livermore National Laboratory, Johns Hopkins, and CEPI.
Airbus partners with Mistral AI to develop sovereign artificial intelligence in the aerospace sector. The partnership aims to integrate secure AI models into the group's operations and processes.
Nvidia invests $300M in Decart, a startup focused on world models and software optimization. Nvidia's participation aims to control an optimization layer capable of running on its chips and those of competitors.
The EU postpones to December 2027 the enforcement of obligations for high-risk AI systems in HR tools. A provisional political agreement on May 7, 2026 regarding the Digital Omnibus AI amends the timeline of regulation 2024/1689.
Defect grading framework for power transmission equipment using MLLM. In-context learning on commercial models, chain-of-thought Q&A generation to reduce manual annotation, then fine-tuning Qwen3-VL-8B via LoRA. SOTA on three grading tasks.
Study on long chain-of-thought traces used for LLM supervised fine-tuning. Researchers identify "harmful continuation": when reasoning continues after the answer is sufficiently supported. Removing these continuations improves fine-tuning outcomes. They propose HCC (Harmful Continuation Cut), a lightweight proxy to detect these boundaries.
BEAMS establishes benchmarks to evaluate AI tools for modeling and simulation. The open-source sd ai project tests multiple LLMs on tasks including causal translation, model iteration, and causal reasoning. Results show AI tools perform better at qualitative discussion than causal reasoning and quantitative error fixing.
M2R (Micro-Macro Retrieval) is a retrieve-while-generate framework reducing hallucinations in long-form LLM generation. It combines macro retrieval (external evidence) and micro retrieval (key information from reasoning) to maintain proximity between factual data and outputs. Trained via reinforcement learning with rule-based rewards.
SovSim, a multi-agent simulation framework, evaluates how 11 state-of-the-art LLMs manage shared resources under asymmetric power structures. Finding: introducing an agent with disproportionate power (boss/king) causes 87.3% degradation in survival rate and cooperation breakdowns compared to symmetric settings.
LLMBridge is an LLM-based system for end-to-end referential bridging resolution in English. The pipeline combines heuristic pre/post-processing with LLM natural language inference capabilities. Evaluated on ISNotes, BASHI, and GUMBridge, it outperforms previous state-of-the-art systems on all three datasets in both end-to-end and gold anaphor settings.
Theoretical paper on stabilizing off-policy temporal-difference learning with function approximation. Proposes BA-TDC and BA-TDRC, replacing TDC's auxiliary matrix with behavior Bellman matrix. Linear analysis with convergence proof under Hurwitz stability condition; experiments on Markov chains and classical counterexamples.
STHTD-MP, a new off-policy temporal-difference method, replaces the covariance metric with the behavior-policy Bellman matrix in the primal-dual saddle-point formulation. Formal convergence analysis and spectral comparison with GTD2-MP show potential gains on benchmarks (Random Walk, Boyan Chain).
Redpanda introduces an Agentic Data Plane architecture using out-of-band metadata channels to enforce security policies, data classifications, and behavioral constraints outside the agent's read/write path. These channels prevent hallucinations and adversarial manipulation while maintaining tamper-proof audit trails. Demonstrated with a multi-agent portfolio rebalancing system.
The Cognitive Categorical Transformer (CCT), a 306M-parameter model augmenting GPT-2 Small, incorporates category-theoretic and cognitive-science-inspired components. On WikiText-103, CCT achieves 21.27 validation perplexity versus 24.19 for GPT-2 Small baseline, a 12% relative reduction (2.92 PPL). Ablations show simplicial message passing accounts for 84% of the improvement.