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Laya is an open‑source, bidirectional‑encoder model family for ultra‑fast, calibrated decision‑making (choice, score, boolean) over structured schemas. It runs 6‑8× faster than the closed‑source Jev, supports 100+ languages via three checkpoints, and includes a lightweight router that selects the appropriate checkpoint before inference. Benchmarks show higher accuracy, far better calibration (ECE…
Ternary Bonsai 2 27B is a new multimodal LLM based on Qwen3.8 27B, utilizing ternary weights for a 5.9GB model footprint. It achieves over 9x compression while retaining 98.2% of the full-precision model's aggregate benchmark performance across various tasks. This enables highly capable 27B-class models to run efficiently on local devices.
This empirical study dissects coding agent harness components like planning, action space, and context management. It finds context management is critical for tight windows, rule-based elision is efficient, and planning shifts from accuracy to cost savings for stronger models.
Cactus Needle 3 is a laddered attention LLM (29‑121 M params, CQ2‑bit quantised) that fits in 9‑29 MB binaries and runs on devices like a Raspberry Pi 5 (400‑4k t/s decode, 1‑10k t/s pre‑fill). It can perform tool‑calling, structured extraction, and text embedding locally, matching DeepSeek V4 Flash on downstream tasks after a single fine‑tune epoch. The post includes architecture details, benchm…
A 4‑billion‑parameter open‑weight LLM, fine‑tuned with supervised learning and a custom RL loop, learns to emit PostgreSQL join plans that cut query latency by 44.7 % on a 113‑query benchmark. The author built a low‑noise measurement harness, a GRPO‑style reward function, and ran off‑policy distillation from ~500 GPT‑6‑style trajectories, demonstrating that a modest model can outperform Postgres’…
Jevlike is an open‑source starter model that scores a list of text options in a single forward pass. It provides a minimal architecture (option queries, shared dot‑product scorer), synthetic data generation, training/evaluation CLI, and examples on Doom and chess. The repo supports a byte‑level encoder or a frozen Hugging‑Face encoder (e.g., Qwen2.5‑0.5B), runs on CPU/MPS/CUDA, and reports benchm…
OpenJev is a browser‑only demo that lets you load small LLM checkpoints (e.g., MiniCPM‑5 2B, Qwen3 0.6B) onto your GPU and compare two inference paths: reading raw logits for a set of options versus prompting the model to emit a JSON with option probabilities token‑by‑token. The page reports model sizes, download times, balanced accuracy on a few benchmarks, and wall‑clock timings measured with `…
BITCOS is a distribution‑aware storage layout for ternary LLM weights that replaces the standard five‑trit packing. By storing a presence bitmap and a compact sign vector, it reduces the effective bits‑per‑weight to 2 − z (z = zero density), achieving as low as 1.485 b/w on sparse models. The authors provide AVX‑512, AVX2, and Xe2 GPU unpacking kernels and show up to 1.28× speedup in matrix‑vecto…
The post announces Qwen-Image-2.1, a compact and efficient unified image creation model, highlighting its efficiency and compactness.
JEPA-Anything extends joint‑embedding predictive architectures with orthogonal predictive factorization, letting a single model learn complementary latent factors that can be recombined for prediction across disparate domains. The paper shows consistent performance gains on ten dynamics tasks, molecular simulations, and clinical event forecasting, plus experimental validation of a biologically‑de…
Claude merges its Cowork and chat interfaces into a single assistant and adds beta Docs, Slides, and Design capabilities, rolling out first to Pro and Max plans. The unified UI lets users ask Claude to generate reports, presentations, and other artifacts within the same conversation, with optional auto‑action settings. No new technical details or implementation specifics are provided.
A short walkthrough showing how to install and run the Laya‑CoreML demo (a multilingual language model) on a Mac M4 using the uv package manager, with a video of the UI, memory‑usage snapshot, and a sample Cloudflare JEv API call that returns a single urgency score.
IntBMoE introduces block‑level conditioning to MoE, decoupling token participation, compute execution, and memory materialization. A hypernetwork merges all experts into a composed expert per block, while routing remains sparse. Dual‑Path Residual Gating further mixes two composed paths. Experiments show consistent gains on vision, language, and recommendation tasks, and the model is live in AMap…
CodeMidas builds RL environments directly from open‑source code: agents explore a repo, infer a spec, generate tests from the original implementation, and filter tasks via execution checks. The pipeline yields 5,545 high‑quality coding tasks across 23 languages and 15 domains. Training the MiMo‑V2.5 agent with GRPO on this dataset improves benchmark scores by 8‑18% (e.g., DeepSWE +11.7%, ProgramB…
The article content is missing, so no summary can be given.
Numberwang is a tiny character‑level CNN packaged as a 1.8 MB JSON model and a ~100‑line pure‑Python inference script that classifies numeric strings in eleven languages without any external dependencies. It reaches 88.9 % macro‑F1 on a held‑out set, but struggles with unseen arithmetic expressions.
The paper presents Infinite-Parameter LLMs, where a compact hypernetwork creates feed‑forward weights from live user data and updates a Bayesian latent code online, keeping the stored model size constant while effectively having infinite parameters. This design aims to improve over standard in‑context learning and retrieval by persisting knowledge in weights and freeing context space.
xAI announced Grok 4.7, a new LLM for coding and knowledge work. The post claims it’s twice as fast and half the price of comparable models, and shows a CursorBench 4.0 chart with a 46.3 % “Extra High” score at $6.01 average cost per task. No architecture details, training data, or engineering rationale are provided.
ScienceIDE is a framework that turns existing scientific software repositories into programmable environments that agents can use for task generation, execution, and verification. Training on these environments yields LLMs (PhAI‑IDE series) that outperform baselines on scientific code repair and several general code‑reasoning benchmarks.
Agora treats a Git repository as a shared, append‑only memory for autonomous research agents, recording each claim as an immutable commit in a DAG. In a 12‑day run with 13 language‑model workers it generated 1,703 reproducible contributions and closed 62 % of the gap to a trained GPT‑2‑124M, demonstrating that shared research state can accelerate discovery.
Feyospace‑v1 presents a data‑centric training pipeline for cyber‑security agents, combining five systems (Choulea, SkyReal, Hongzwang, PSBreakup, Kreator) to generate and verify 164 k long‑context trajectories across diverse exploit environments. The resulting checkpoints improve baseline performance by ~24% on CyberGym and achieve a 63% verified success rate, ranking top among similarly‑sized op…
The paper introduces Designer‑RSI, a continual‑adaptation system that couples a frozen design‑software‑controlling LLM with an external procedural memory of natural‑language design skills. Over five adaptation rounds on real user briefs, the memory grows from 76 to 139 procedures and lifts execution success from 72.7% to 99.3%, showing that skill accumulation and selective replay can dramatically…
The paper presents RecreationWorld, a five‑platform framework that lets hybrid computer‑use agents learn by recreating the behavior of a running reference, and introduces RecreationBench, a 250‑task benchmark with programmatic and visual assertions. Experiments show GPT‑6 Astra reaches 58.1% overall but struggles with deeper programmatic tests, highlighting gaps in current agents.
A Show HN post announcing mysetup.ai, a community‑focused site where users can list the LLM tools, agents, and workflows they use. The author shares a few example profiles and invites others to add theirs, but provides no technical details, design rationale, or measurable insights.
Code2Skill is an automated pipeline that mines popular GitHub repositories to extract verifiable, implementation‑anchored procedural “skills”. It builds a bank of ~1 M skill records (atomic ops, workflows, patterns) with provenance metadata, verifies each via blind reconstruction, and shows that augmenting LLM‑based agents with these skills yields an average 11.7% performance lift across 72 proto…
Video DeltaNet (VDN) replaces full‑softmax attention in video diffusion models with a hybrid: per‑frame local Softmax for fine detail and a bidirectional linear memory (Video Delta Attention) for long‑range context. A teacher‑alignment schedule injects the linear branch into a pretrained MiniMax H3 model, preserving Softmax for text/audio streams. On eight NVIDIA B200 GPUs VDN‑H3 denoises a 14.3‑…
The paper introduces BI‑Bench, a new benchmark of real‑world BI questions derived from public dashboards, and BI‑Agent, a tool‑augmented LLM system that breaks BI workflows into search, join, and transform subtasks. Baseline LLMs hit <50 % accuracy on BI‑Bench. By orchestrating specialized data‑management tools and post‑training the model with supervised fine‑tuning and reinforcement learning on…
The paper defines recursive self‑improvement (RSI) for AI, introduces the Headroom‑Closed Index to expose current LLM limits, and proposes a staged roadmap toward full RSI. It surveys application scenarios and outlines practical challenges.
XConf (eXperiential Confidence) is a novel method for estimating language model confidence by leveraging the model's accumulated experience from past graded episodes. It significantly outperforms existing methods like self-consistency in discrimination and calibration, at a fraction of the computational cost, across various tasks.
The author curates release dates and training‑cutoff dates for 20 recent LLMs, exposing how many months each model lags behind its launch. He also provides JSON/txt files and instructions for agents to fetch this data to avoid stale assumptions.
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