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Expert-Space Exploration in MoE Reinforcement Learning

ESRL explores MoE expert-routing space during RL, lifting Qwen3-30B-A3B Pass@1 by 3.2 points over GRPO at no extra cost.

ESRL (Expert-Space Exploration Reinforcement Learning) is an architecture-aware framework that treats expert routing in Mixture-of-Experts models as an additional source of rollout diversity. It preserves high-confidence experts as anchors, restricts stochastic routing to a plausible candidate pool, adapts perturbation strength to router entropy, and replays recorded expert paths during policy optimization. ESRL achieves the best performance across top-K, top-1, and shared-expert routing backbones on mathematics, science, and code tasks; on Qwen3-30B-A3B it improves average Pass@1 and Pass@8 over GRPO by 3.2 and 4.5 percentage points respectively, without additional sampling or compute.

arXiv cs.AI / cs.LG / cs.CL · 5d agoAI research 2 sources

Decline in Rig Exploit Kit

Rig exploit kit activity dropped roughly 75% after the pseudo-Darkleech and EITest campaigns stopped using EKs, reflecting an overall decline in exploit kit activity.

Rig EK activity fell sharply in 2017: the pseudo-Darkleech campaign disappeared at the end of March, cutting Rig traffic about 50%, and the EITest campaign switched to tech support scams in late April, cutting another 50% in May. Broader causes include a shrinking browser target base, no major EK zero-day in over a year, and community takedowns of domain shadowing infrastructure. Criminals are shifting to malspam, social engineering schemes like fake HoeflerText notifications, and tech support scams.

Palo Alto Unit 42 · Aug 17, 2026Exploit / PoC in the wild1

Lightning Weave: Improving the Accuracy-Efficiency Frontier of Reasoning Models through Capability Composition

Lightning Weave composes capabilities from independently post-trained models via on-policy distillation, improving Qwen3.5-4B reasoning accuracy while cutting tokens.

Lightning Weave is a post-training framework that merges accuracy and efficiency capabilities from independently post-trained specialist models into a single student via on-policy distillation. Each capability is represented as a policy shift, combined via aligned log-ratio shifts and Tilted-Target DOPD, enabling training without serving multiple live anchor models concurrently. On Qwen3.5-4B, it raises HMMT 2025 accuracy from 59.2% to 64.0% with 10.7% fewer response tokens, and LiveCodeBench v5 accuracy from 41.7% to 54.2% with 9.6% fewer tokens. The authors report a state-of-the-art accuracy-efficiency Pareto frontier across diverse students and math/code benchmarks, with code planned for release.

Hugging Face daily papers · 4d agoAI research

CryptoBit: Another Ransomware Family Gets an Update

Palo Alto Unit 42 analyzes updated CryptoBit ransomware distributed via the Rig exploit kit, documenting IOCs, eight sample hashes, and disguise tactics from June 2016.

Unit 42 observed an updated CryptoBit ransomware variant (also tracked as CriptoBit or Mobef) delivered through the Rig exploit kit via the gate domain realstatistics.info between June 17-27, 2016. Infections targeted Windows hosts with out-of-date Flash plugins, and post-infection callbacks used fake user-agents and referrers to disguise traffic to laoismacau.com. At least eight samples were collected, and by June 27 the campaign had begun distributing other malware.

Palo Alto Unit 42 · Aug 17, 2026Ransomware in the wild

HarnessVLN: Unifying Training-Free Embodied Navigation through an Agent Harness

Researchers introduce HarnessVLN, a zero-shot training-free agent harness that sets new training-free SOTA on vision-language navigation benchmarks including R2R and HM3D.

HarnessVLN is a zero-shot, training-free framework for embodied vision-language navigation that coordinates perception, retrieval, grounding, navigation, recovery, and termination through a unified tool interface. It validates planner proposals against spatial evidence, geometric feasibility, and subgoal consistency, using hierarchical event memory and a persistent Spatiotemporal Graph that stores reusable spatial evidence and failure annotations. It reports success rates of 60.8% on R2R, 53.9% on RxR, 76.0% on HM3D-v2, and 59.3% on HM3D-OVON, surpassing prior training-free state of the art, with real-world humanoid deployment demonstrated.

Hugging Face daily papers · 3d agoAI research

Campaign Evolution: EITest from October through December 2016

Unit 42 details the EITest campaign's late-2016 changes: gates and obfuscation dropped, with Rig EK variants delivering ransomware and infostealers.

Unit 42 tracks the EITest campaign through late 2016, noting it abandoned the gate between compromised websites and exploit kit landing pages after its October 2016 report, and stopped obfuscating injected script URLs by October 15. The campaign primarily uses the Rig-E (Empire Pack) variant and sometimes Rig-V of Rig EK to deliver Cerber and CryptoMix ransomware plus infostealers such as Gootkit, the Chthonic banking Trojan, Ursnif variants, and Latentbot. Tracked since 2014 by Malwarebytes Labs and others, EITest targets unpatched Windows systems without specific victimology.

Palo Alto Unit 42 · Aug 17, 2026Threat actor in the wild

Kaininja: Extending Native 3D Generators to the Part Level

KaiNinja extends TRELLIS.2 native 3D generation to part-level assets via a dual-volume O-Voxel representation, cutting whole-object Chamfer distance by 40%.

KaiNinja extends the TRELLIS.2 native 3D generator to produce part-level assets instead of one fused mesh, enabling downstream editing, rigging, and simulation. A dual-volume form of the O-Voxel representation solves the problem that a single volume cannot represent interfaces where two parts touch. The model needs no segmentation network, is partly trained on LLM-agent-authored part data, lowers whole-object Chamfer distance by 40%, and raises strict part F-score by 16% versus other part-generation pipelines.

Hugging Face daily papers · 3d agoAI research

EITest Campaign Evolution: From Angler EK to Neutrino and Rig

Unit 42 details the EITest campaign's shift from Angler to Neutrino and Rig exploit kits while distributing ransomware, downloaders, and banking trojans.

Unit 42 updated its tracking of the EITest campaign, first identified in October 2014, which compromises websites with injected scripts that redirect victims through a gate to exploit kits. After Angler EK disappeared in June 2016, EITest switched to Neutrino and then primarily used Rig EK by August 2016. In September 2016 the campaign began using hex-obfuscated JavaScript and simplified gate URLs, while continuing to distribute payloads including Gootkit, Cerber, Bart, CryptFile2, Vawtrak, Ursnif, and Tinba. Gate infrastructure consistently reused IP blocks such as 85.93.0.0/24 even as domain names changed.

Palo Alto Unit 42 · Aug 17, 2026Threat actor in the wild

Import AI 470: No rights for machines; automating environment generation with SPADE; and building better GPU kernels with Hawkeye

METR analysis finds AI accelerating cyber vulnerability discovery, while SPADE self-play environment generation improves Qwen3 reasoning benchmark scores at 30B scale.

Import AI 470 discusses a METR research note reporting differential acceleration from AI: major acceleration in reported cyber vulnerabilities (cURL, OpenSSL, Firefox, Microsoft, NVD, OSV), minor acceleration in mathematics, and no measurable acceleration in AI-research optimization benchmarks. It also covers SPADE, a self-play framework from a multi-university team (University of Washington, Stanford, MIT, CMU, and others) that co-evolves executable training environments and agent capability using Environment Designer and Reasoning Agent roles with hint-based regret rewards. Trained on Qwen3-4B-Instruct-2507, Qwen3-8B, and Qwen3-30B-A3B-Instruct-2507 via GRPO (400 rollouts of 25 environments), SPADE lifted the 30B-A3B game-environment suite average to 58.3, +8.1 over base, and improved tool-use results across backbones. The issue also references Hawkeye for building better GPU kernels.

Import AI · 23d agoAI research1

Campaign Evolution: pseudo

Unit 42 traces the pseudo-Darkleech campaign's 2016 shift from Angler to Neutrino to Rig exploit kits and rotating ransomware payloads.

Unit 42 documents how the pseudo-Darkleech exploit kit campaign evolved through 2016, switching from Angler EK to Neutrino EK in June and to Rig EK in September after Neutrino ceased operations. Payloads rotated from TeslaCrypt to CryptXXX, CrypMIC, and finally Cerber ransomware by October 2016. Injected script on compromised websites changed from 12,000-18,000 character obfuscated blocks to short, unobfuscated hidden iframes starting July 1, 2016.

Palo Alto Unit 42 · Aug 17, 2026Threat actor in the wild

Cadence: Error-Bounded Lossy Compression of Demand Time Series with a Time-Series Foundation Model

Cadence pairs Google's 330M-parameter TimesFM-3 foundation model with adaptive arithmetic coding, gaining 13-28% on 2026 demand series over classical predictors.

Cadence is an error-bounded lossy compressor for numeric time series combining the 330M-parameter Google TimesFM-3 foundation model with an adaptive arithmetic coder, guaranteeing a per-sample error bound. On 49 EIA-930 balancing-authority demand series from 2026 it gains 13.3% over the best of six classical predictors and 28.3% on 50 MTA ridership series, winning all 297 series-tolerance pairs with a 21.4% median gain. The paper also reports negative results, including that foundation models add negligible value for lossless coding and that PyTorch predictions are not bit-identical across batch sizes.

Hugging Face daily papers · 12d agoAI research1

Afraidgate: Major Exploit Kit Campaign Switches from CryptXXX Ransomware Back to Locky

The Afraidgate exploit kit campaign stopped distributing CryptXXX and now delivers the .zepto Locky variant exclusively through Neutrino EK since July 2016.

By mid-July 2016, the Afraidgate campaign switched from CryptXXX ransomware to consistently delivering the .zepto (Zepto) variant of Locky, using the Neutrino exploit kit after Angler EK disappeared in early June 2016. The campaign starts from compromised websites with injected scripts that redirect to Afraidgate domains and then Neutrino EK landing pages on .top domains. Zepto emerged after the Necurs botnet returned from a three-week outage, bringing new anti-sandboxing and evasion techniques.

Palo Alto Unit 42 · Aug 17, 2026Ransomware in the wild

Scalability Analysis of Distributed Kolmogorov-Arnold Network Training on High-Performance Computing Systems

An empirical study shows distributed Kolmogorov-Arnold Network training reaches 74.7% parallel efficiency at 8 A100 GPUs, with overheads driven by All-Reduce choices.

The study evaluates data-parallel Kolmogorov-Arnold Network (KAN) training on the FinisTerrae III supercomputer using up to 8 NVIDIA A100 GPUs across 4 nodes with PyTorch Distributed Data Parallel. Strong scaling yields 5.97x speedup and 74.7% parallel efficiency at 8 GPUs, comparable to conventional deep learning workloads, while communication overhead ranges from 1.3% to 6.1%, driven mainly by All-Reduce algorithm selection and inter-node latency rather than KAN's edge-wise gradient structure. Weak scaling shows an initial single-to-multi-GPU throughput drop followed by stability, and the parameter-to-memory ratio improves with model size even as training time scales unfavorably. The authors provide GPU topology and model-size deployment guidelines for KAN training.

arXiv cs.AI / cs.LG / cs.CL · 9d agoAI research1

Campaign Evolution: Darkleech to Pseudo

Unit 42 traces the pseudo-Darkleech campaign, which compromises websites to inject scripts redirecting visitors to exploit kits delivering ransomware.

Palo Alto Networks Unit 42 analyzed the evolution of the pseudo-Darkleech campaign, which injects malicious script into compromised Apache, IIS and WordPress sites to redirect visitors to exploit kits such as Angler and Neutrino. The original Darkleech Apache module infected thousands of servers starting in 2012 and delivered Blackhole EK until that kit disappeared after Paunch's 2013 arrest. From 2015 onward, pseudo-Darkleech delivered ransomware families like CryptoWall and TeslaCrypt, and by early 2016 its injected scripts added obfuscated numeric blocks with frequently changing separator characters. Unit 42 tracks these patterns to help defenders identify compromised websites.

Palo Alto Unit 42 · Aug 17, 2026Threat actor in the wild

Ambient @ EgoLongQA 2026: Distilling Long-Video perception into a Sub-2B Model

Ambient team wins EgoLongQA 2026 sub-2B division by distilling an agentic long-video perception pipeline into a 2B vision-language model.

Ambient's entry to the EgoLongQA track of the Wearable-AI Challenge at ECCV 2026 placed first in the <=2B parameter division with 0.8279 on the held-out test set. The system distills the junior perception module of a tool-using agentic pipeline into a 2B student, reaching 89% of the pipeline's accuracy with 1.1% of its parameters and lifting a 27.1% base model to 81.4%. To meet the division limit, the multilingual embedding table is pruned from 248,320 to 143,469 rows, reaching 1.9985B parameters with provably identical logits on retained rows.

Hugging Face daily papers · 7d agoAI research

Can LLMs Engineer Their Own Agent Harness? ByteDance Seed’s HarnessDev Says Only 34 of 64 Changes Generalize

ByteDance Seed's HarnessDev benchmark finds LLM-built agent harnesses trail human engineering on code and search, with only 34 of 64 revisions generalizing.

Researchers from ByteDance Seed, SUTD, Georgia Tech, M-A-P, and TokenWave.AI introduce HarnessDev, a benchmark that evaluates the runnable agent harness an LLM writes rather than its answers, using Creation and Evolution stages across SWE-bench Pro, Terminal-Bench 2.1, MLE-bench, EQ-Bench3, and BrowseComp (2,207 instances). Six creator models including Opus 4.8, GPT-5.5, Gemini 3.1 Pro, DeepSeek V4 Pro, Qwen 3.7 Max, and Seed 2.0 Pro were tested; Opus 4.8 posted the best average of 67.8 versus an 86.2 human-engineered reference. Self-built harnesses beat references on writing and ML experimentation but lag badly on code and search, and quality proved executor-specific: Opus 4.8 fell from 69.3 to 33.0 on SWE-bench Pro when the executor was switched to Gemini. Evolution gains were small and noisy: of 64 adjacent changes, feedback and held-out scores agreed only 34 times (53.1%), and much generated state and memory code never executed.

MarkTechPostupdated · 11h agofirst · 5d agoAI research 18 sources

Akamai Valkey Managed Database: Real-Time Memory for Enterprise AI

Akamai launched Valkey Managed Database, a low-latency in-memory data layer aimed at cutting AI inference costs and accelerating RAG.

Akamai introduced Valkey Managed Database, a managed in-memory data service based on the open-source Valkey project. The company positions it as real-time memory for enterprise AI, optimizing inference costs, accelerating retrieval-augmented generation, and powering real-time AI agents.

Akamai Blog · 29d agoAI tools & infra

ENCP: Episode-Normalized Conformal Prediction for Vision-and-Language Navigation

ENCP calibrates conformal prediction per navigation episode, giving step-level coverage guarantees for vision-language navigation agents despite within-episode dependence.

Episode-Normalized Conformal Prediction (ENCP) rescales a nonconformity score by a VLN policy's residual confidence and calibrates one maximum score per episode, preserving step-level coverage of at least 1−α despite dependence among steps within an episode. Across four VLN policies and three nonconformity scores on R2R and REVERIE, ENCP meets all reported empirical step-coverage targets in seen-to-unseen evaluation. The model-agnostic uncertainty estimates can signal when an agent should defer to a stronger predictor or human assistance.

arXiv cs.AI / cs.LG / cs.CL · 1d agoAI research

[AINews] AEF-1 standard emerges for Third Party Evaluators, as Xai, OpenAI, and Anthropic all cosign

xAI, OpenAI, and Anthropic cosign the AEF-1 third-party evaluation standard while Dario Amodei proposes embedded evaluators for safety verification.

The AI Evaluator Forum published AEF-1, a baseline standard for independent third-party AI evaluations covering access, conflicts of interest, funding relationships, recusal, and transparency, cosigned by xAI, OpenAI, and Anthropic. Dario Amodei wrote a rare personal blogpost proposing embedded evaluators such as METR with desks, badges, company laptops, and internal-risk-team-level access to verify safety commitments, plus democratic and global coordination frameworks. The roundup also covers the pacing debate: Bilal Chughtai left Google DeepMind arguing progress may outrun alignment, while critics including Aidan Gomez and Cohere push back against slowdowns and lab gatekeeping. Additional items include Cline Desktop's launch with open-weight model support.

Latent Space · 1d agoAI safety & security

NVIDIA Open-Sources OSMO: One YAML Orchestrates Physical AI Training, Simulation, and Robot Testing

NVIDIA open-sourced OSMO, a Kubernetes-native YAML orchestrator running physical-AI training, simulation, and robot testing across mixed GPU tiers.

OSMO (Apache-2.0, latest release 6.3.1) lets teams describe training, simulation, and hardware-in-the-loop pipelines in a single YAML and routes tasks across datacenter GPUs (GB200), workstation RTX hardware, and edge devices like Jetson AGX Thor. It ships Helm charts and containers on NGC, uses the KAI Scheduler with NVLink topology-aware placement, and includes RBAC, OAuth2, and TLS termination. NVIDIA says it is battle-tested on GR00T, Isaac Lab, Isaac Sim, and Isaac ROS, and integrates with Claude Code, OpenAI Codex, and Cursor agents.

MarkTechPost · 2d agoAI tools & infra

Design Docs Are All You Need: An AI-native Machine-Learning Performance Tool

Researchers present SMART, an ML performance-modeling library regenerated by AI coding agents from natural-language design docs instead of code.

The paper describes SMART, a symbolic performance-modeling library whose main branch contains almost no code: the repository is a DAG of self-contained design documents, and coding sub-agents regenerate implementations from only the docs on version updates. Reliability rests on a worked-example doc style used as in-context demonstrations and a minimal operator IR with SymPy cost expressions, offering both fast analytical roll-up and fine-grained modulo-scheduling modes. Regenerated implementations reproduce hand-audited reference models, including DeepSeek-V3 serving on a TPU pod slice, to round-off precision.

arXiv cs.AI / cs.LG / cs.CL · 12d agoAI research1

Latent-MoE: Domain-Aware Mixture-of-Experts for PDEs with Multi-Regime Physics

Latent-MoE adds domain-aware mixture-of-experts routing to PINNs, improving accuracy over an order of magnitude on multi-regime physics PDEs.

The paper shows standard coordinate networks used in physics-informed neural networks have translation-variant NTKs causing long-range gradient conflicts on PDEs with spatially varying physics. MoE architectures with centered compact-support routers produce a uniformly banded NTK that localizes learning. Latent-MoE interleaves domain-aware MoE blocks in a shared backbone, outperforming FB-PINNs and X-PINNs by over an order of magnitude on multi-stage time-variable physics benchmarks.

arXiv cs.AI / cs.LG / cs.CL · 9d agoAI research

AI Infra Summit: NVIDIA Vera Rubin and DSX Platform Advancements Showcase Energy Efficiencies of Optimizing Tokens Per Watt for AI Factories

At AI Infra Summit, NVIDIA showcased Vera Rubin and DSX gains up to 1.4x tokens per megawatt, plus Annapurna, d-Matrix, and Pinterest partnerships.

Ian Buck's AI Infra Summit keynote before 8,000+ attendees emphasized validated agentic tokens per megawatt as the emerging AI infrastructure metric. Announcements include Amazon's Annapurna Labs collaborating on NVHBM custom high-bandwidth memory, d-Matrix integrating NVLink Fusion with Raptor XPUs, and Pinterest using Blackwell plus Dynamo inference software for conversational visual discovery. Lambda reported 23% better performance per watt with DSX MaxLPS on Blackwell servers, running 19 nodes on a 16-node power budget. NVIDIA says DSX MaxLPS combined with Groq 3 LPX on Vera Rubin NVL72 targets up to 35X token throughput per megawatt versus GB200 NVL72 for 2-trillion-plus-parameter models.

NVIDIA Blog · 1d agoAI industry

Former OpenAI researcher builds an AI model that judges options instead of writing text

TypeSafe AI launches Jev, a judgment-only model built by ex-OpenAI staff that classifies inputs with 70-500 ms latency instead of generating text.

Startup TypeSafe AI, co-founded by former OpenAI researcher and InstructGPT co-author Diogo Almeida, introduced Jev, a model that scores developer-defined answer options with probabilities rather than generating free-form text. The company claims 70-500 ms responses, parallel multi-question evaluation, and $0.042 per million input tokens with free outputs, targeting request routing, sales intent scoring, and assistant guardrail checks. Benchmarks are self-built and not independently verified, the 'no hallucination' guarantee only covers output structure, and access is currently via waitlist.

The Decoder · 9h agoAI industry

X-AuT: Progressive Audio-Encoder Compression for Speech LLMs with Cross-Scale Distillation

XPeng AI's X-AuT prunes speech LLM audio encoders, cutting Qwen3-ASR-0.6B error from 5.61% to 5.27% with fewer parameters.

X-AuT is a progressive compression framework for speech LLM audio encoders that selects layer combinations via short behavioral probes and restores pruned models using cross-scale distillation and LoRA finetuning while keeping the language-model backbone frozen. Compressing Qwen3-ASR-0.6B from 18 to 16 audio-encoder layers lowered macro-average error from 5.61% to 5.27% on ten Chinese-English benchmarks. A 14-layer model reached 5.75% error with 20.7% fewer audio-tower parameters, and progressive pruning outperformed direct pruning (5.75% vs 6.73%).

Hugging Face daily papers · 7d agoAI research

LACE: Layer-Wise Compression for Dynamic Frame Rate Codecs

LACE introduces layer-wise compression for dynamic frame rate audio codecs, cutting sequence lengths and speeding TTS inference while preserving quality.

LACE (Layer-Adaptive Codec Encoding) applies an independent compression step at each quantization layer of a neural audio codec, enabling layer-specific segmentation boundaries instead of shared ones. Union alignment and boundary anchor mechanisms keep durations consistent for downstream text-to-speech. On LibriTTS, LACE achieves a better rate-quality tradeoff than prior dynamic frame rate codecs and improves TTS inference efficiency at competitive synthesis quality. Code is released in the ESPnet3 codec recipe.

arXiv cs.AI / cs.LG / cs.CL · 1d agoAI research1

TART: A Modular Tool for Technique-Aware Audio-to-Tablature Guitar Transcription

TART pipeline transcribes guitar audio to tablature with fingering and technique labels, outperforming prior baselines by up to 8.5 Tab F1 points.

TART is a modular four-stage pipeline for audio-to-tablature guitar transcription, combining an audio-to-MIDI transcription model, an expressive technique classifier, an audio-conditioned T5 encoder-decoder for string-fret assignment, and an automated tablature generator. In zero-shot evaluation on GuitarSet, EGDB, and two noisy augmented benchmarks, it averages 81.35% audio-to-MIDI F50 (+6.67 over the best baseline), 71.8% string-fret Tab F1 (+8.5), and 54.08% end-to-end Tab F1. The authors state it is the first framework to generate tablature with both fingering and expressive technique annotations directly from guitar audio.

arXiv cs.AI / cs.LG / cs.CL · 6d agoAI research2

Roland is getting into generative AI music with Melody Flip

Roland launched Melody Flip, a DAW plugin that generates genre-themed MIDI loops rather than complete songs like Suno or Udio.

Roland's Melody Flip is a DAW plug-in offering around 250 genre-based 'Palettes' for generating melodies, chord progressions, basslines, and drum patterns, either from scratch or derived from a reference track. Users can control genre, note density, BPM, and key but cannot use text prompts, and outputs are simple loops with General MIDI-style tones intended for MIDI export into a DAW. The launch follows well-received hardware releases like the SH-4d, Gaia 2, and TR-1000, though music-community sentiment toward generative AI may limit goodwill.

The Verge · AI · 12d agoAI industry

Agent Harness vs Agent Framework vs MCP: Which Layer Owns the Loop, State, Tools, Permissions, and Recovery

Architecture explainer separates agent harnesses, frameworks, and MCP by which layer owns the loop, state, permissions, and recovery.

The article distinguishes agent harnesses (OpenAI Codex, Claude Agent SDK), which own the execution loop, sandbox, permission model, and recovery; frameworks (LangGraph, OpenAI Agents SDK, Microsoft Agent Framework), which supply composable primitives; and MCP, a stateless JSON-RPC wire protocol governed by the Linux Foundation's Agentic AI Foundation since December 2025. An ownership matrix maps the execution loop, state, tool transport, permissions, recovery, sandboxing, and multi-agent orchestration to each layer. The 2026-07-28 MCP specification made the protocol fully stateless, retiring the initialize handshake and session headers.

MarkTechPost · 2d agoAI research1

IBIB: A Protocol for Measuring Enterprise AI Systems by Serving Route, Not Model Identifier

IB2 protocol scores enterprise AI systems by serving route with reliability-inclusive scoring; serving-arm choice moved one score from 77.38 to 82.54.

The protocol has three parts: a gold-blind capability-binding preflight verifying a route can execute the evaluation contract, a reliability-inclusive first-pass scoring rule, and structurally score-blind adjudication. Its reference instantiation uses 128 locked tasks and 987 assertions over document, spreadsheet, chart, tool, and database work, released as procedure and schemas rather than an exposed corpus. Across eleven systems, two complete runs on identical weights later failed distinct binding-gate predicates, four of seven suites saturate within a six-system band driven by governed database work and multi-tab joins, and excluding failed responses from denominators changes the point ordering. Serving-arm choice shifted one declared revision and precision from 77.38 to 82.54, though arms differed in access mode, harness generation, and the tool-call parser.

arXiv cs.AI / cs.LG / cs.CL · 7d agoAI research1

Building a Production Greek-English Speech Recognizer

Engineering report details Sophea, a production Greek-English ASR reaching 4.26% WER on public English sets via ROVER ensemble and data-pipeline calibration.

Across 23 training iterations, two architectures, and nine production gates, no single data composition passed all gates; a three-model ROVER ensemble reached 9 of 9 gates and cut overlapping-speech WER from 53.35% to 37.87%. Calibrating an audio-quality filter against in-domain anchors reduced discarded scored Greek audio from 98.7% to 10.6%, and a pre-registered ablation traced a hallucination defect to one training-data package. The sophea/asr-k1 preview arbiter lists 4.26% average WER on eight public English test sets and 25.88% WER on live Greek noisy traffic; no weights or training data are released.

Hugging Face daily papers · 6d agoAI research

VEX-Bench: Benchmarking LLM Agents for Assessing Exploitability of Software Supply Chain Vulnerabilities

Introduces VEX-Bench, 75 expert-labeled real-world cases testing whether LLM agents can assess supply chain vulnerability exploitability; frontier models reach about 80% F1.

VEX-Bench is the first benchmark evaluating LLM agents on assessing whether upstream dependency vulnerabilities are exploitable in downstream projects, with 75 real-world expert-labeled cases across Python, Java, and Go mined from GitHub. Nine models across three agent harnesses were evaluated; GPT-5.5 and Claude Opus 4.6 reach approximately 80% F1 on binary vulnerability-status classification, but only GPT-5.5 surpasses 70% macro-F1 on fine-grained justification classification. The gap highlights the difficulty of moving beyond binary exploitability calls to explaining exploitability reasons, unlike prior benchmarks targeting zero-day settings.

arXiv cs.CR · 9d agoResearch1

TokenRhythm/NeoHorse-1-4B — new model trending #30 on Hugging Face

TokenRhythm releases NeoHorse-1-4B, an Apache-2.0 agentic fine-tune of Qwen3.5-4B claiming +5.93 benchmark macro-average gain.

NeoHorse-1-4B is a roughly 4B-parameter text-only causal language model post-trained by TokenRhythm from Qwen/Qwen3.5-4B for agent harnesses, tool use, coding, and instruction following. It applies routing-guided curriculum SFT and routing-guided on-policy distillation over execution trajectories as an early prototype toward recursive self-improvement (RSI). The release reports a 64.87 macro average across ten benchmarks versus 58.94 for Qwen3.5-4B (+5.93) and is distributed under Apache-2.0, trending #30 on Hugging Face.

Hugging Face trending models · 11d agoModel release1

Context Engineering Inside the Harness: 4 Mechanisms That Beat Context Overflow and Goal Loss on Long-Horizon Tasks

Survey of four harness mechanisms—context budgeting, compaction, todo-state, and memory—that keep long-horizon LLM agents on task across 200+ tool calls.

The article details how agent harnesses, not larger context windows, solve context overflow and goal loss on long-horizon tasks, citing Chroma's Context Rot report showing 18 LLMs (GPT-4.1, Claude 4, Gemini 2.5, Qwen3) degrade on long inputs. Concrete implementations include LangChain Deep Agents offloading tool responses over 20,000 tokens to the filesystem and truncating old tool calls at 85% window usage, and Claude Code capping auto memory at 25KB while re-reading the 5 most recently modified files after compaction. OpenAI's Responses API now offers server-side compaction via context_management with a standalone /responses/compact endpoint, which Codex uses for long-running coding tasks. Manus reports a roughly 100:1 input-to-output token ratio per ~50-tool-call task, motivating todo.md state recitation to prevent goal drift.

MarkTechPost · 3d agoAI research1

Sakana AI Researchers Introduce PC-ALM, a Layer-Local Alternative to Backpropagation That Trains 1000-Layer Networks

Sakana AI's PC-ALM adds per-layer Lagrange multipliers to predictive coding, matching backprop on networks up to 1000 layers with layer-local updates.

Sakana AI researchers propose Augmented Lagrangian Predictive Coding (PC-ALM), a training method that keeps every update layer-local while recovering backprop-aligned credit signals. The team proves multipliers converge to exact backprop adjoints in linear networks and trains 1000-layer residual MLPs on MNIST within about 2 points of backprop accuracy. PC-ALM matched backprop across a width/depth grid from 8 to 128 on MNIST and Fashion-MNIST where standard predictive coding failed in deep, narrow networks, and improved over PC on ResNet-18 with CIFAR-10 and Tiny ImageNet. An MIT-licensed JAX reference implementation reproduces the results on CPU.

MarkTechPost · 2d agoAI research1

Deep Learning-Based Detection of Electrical Faults and Power Quality Disturbances in Aerospace Power Systems

Compact ResNet detects electrical faults in 400 Hz aircraft power systems with 95.87 percent accuracy after deployment on Xilinx Zynq MPSoC.

The study targets multiclass fault and power quality disturbance detection in 400 Hz aerospace networks using a high-fidelity simulation model inspired by the Boeing 787 electrical architecture, covering 21 normal, disturbance, switching, open-circuit, and short-circuit conditions. Two 73,500-sample datasets are built from 1D waveforms and STFT time-frequency representations, augmented with domain randomization and class-specific GANs, and the time-series dataset is released via IEEE DataPort. A compact ResNet with 175,685 parameters achieved 96.94 percent software test accuracy, and 95.87 percent after 8-bit quantization on a Xilinx Zynq UltraScale Plus ZCU102 with 6.90 ms mean accelerator latency per record.

arXiv cs.AI / cs.LG / cs.CL · 7d agoAI research