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JustFit: 200K-Token LLM Serving on a 24 GiB Laptop with Just-in-Time State Management
JustFit MLX runtime serves 200K-token contexts for Qwen3.8-27B on a 24 GiB MacBook via just-in-time state management.
JustFit is an MLX-based inference runtime combining KVExec for compressed KV execution, PhaseSwap for component residency, and StateTrans for state-preserving serving transitions, independent of weight quantization. On a 24 GiB M4 Pro MacBook running Qwen3.8-27B MXFP4, it completed 196,608 input and 16,384 output tokens, raising single-request context from the mlx-vlm baseline's 30,720 positions to 212,992 (6.93x). Performance tests show 19.11 tokens/s on a 32K-input probe with a 16,374 MiB median peak footprint, and the runtime answered 29 of 30 AIME 2026 problems correctly.
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Hackers Actively Exploit Critical WooCommerce Plugin Vulnerability to Upload PHP Backdoors
Attackers actively exploit CVE-2026-27540 (CVSS 9.8) in WooCommerce Wholesale Lead Capture plugin to upload PHP webshells; patch shipped in version 2.0.3.2.
CVE-2026-27540 (CVSS 9.8) in the WooCommerce Wholesale Lead Capture plugin affects versions 2.0.3.1 and earlier across roughly 6,000 active installations. The unauthenticated AJAX handler wwlc_file_upload_handler trusts a client-supplied file_settings allowlist, letting attackers upload shell.php for remote code execution. Wordfence has blocked more than 100,000 exploit attempts since disclosure, with spikes in June, July, and August 2026. The vendor fixed the flaw in version 2.0.3.2.
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One Exploit Chain, Two Espionage Campaigns: Chrome and Windows Under Fire
Two China-linked APT groups reused identical Chrome/Windows zero-day chain against NGOs, deploying GRIMWIDGE backdoor and LONGTALE credential-stealing extension.
Volexity reports that China-linked actors UTA0560 and JungleBamboo (APT31/TA412) ran byte-identical Chrome/Windows exploit chains against NGOs starting September 1, 2026, combining Chrome type confusion CVE-2026-85046, WebAssembly sandbox escape CVE-2026-87491, and Windows kernel flaw CVE-2026-85880. The Chrome bug was fixed in Chromium source but not yet shipped to Chrome users, making it an effective zero-day with an unusual patch gap. UTA0560 delivered the in-memory GRIMWEDGE JScript backdoor, while JungleBamboo deployed the SUPERSTOMP loader installing LONGTALE, a malicious Chrome extension disguised as Google Gemini that steals cookies, session tokens, and keystrokes. Volexity assesses with low confidence the exploit chain was sold or shared among different Chinese end-users.
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[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.
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The Router Within: Eliciting Native Skill Routing from a Frozen LLM
Gavel reads skill-routing signals from a frozen LLM's forward passes with two linear maps, beating retrieval pipelines by up to 21.9 points.
Gavel (Glance And Verdict) shows a frozen agent LLM already contains skill-routing signals in its forward passes, read out via two trained linear maps without loading skill text into context. A glance step scores the full library using mid-layer states and per-skill banks built in one forward pass; a verdict step fuses the model's own likelihood and yes/no judgment as a product of experts. Trained once, it transfers zero-shot to three public benchmarks and SkillTraj (372 simulated agent trajectories); on Qwen3-32B it beats progressive disclosure and retrieve-and-rerank pipelines adding 1.2B-16B external parameters by up to 13.4 points (21.9 mid-rollout).
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SlipSense: Multimodal Tactile Learning for Low-Latency and Generalized Slip Detection
SlipSense fuses a 32x32 piezoresistive array and MEMS accelerometer to detect robotic grip slips within 23.1 ms, generalizing zero-shot across platforms.
SlipSense is a multimodal tactile slip-detection framework built on TacV5, a sensor combining a 32x32 piezoresistive array at 240 Hz and a 3-axis MEMS accelerometer at 8 kHz. It performs modality-specific encoding, intra-sensor fusion, and cross-modal attention with causal temporal prediction at 240 Hz. On a 1.4-million-frame dataset spanning 37 objects it achieves 96.7% Macro F1 with a false-positive rate below 1.6%, detecting 76% of slip events within 23.1 ms. Trained solely on UMI data, it transfers zero-shot to a Tesollo dexterous hand across unseen objects, sensor units, and platforms.
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Why don't machine learning research agents overfit?
Amazon researchers explain why ML research agents avoid benchmark overfitting, attributing generalization to compressibility of successful strategies.
Amazon Science summarizes the paper "What fits (into few tokens) doesn't overfit: Compression and generalization in ML research agents," which investigates why benchmark hill-climbing loops, whether run by human communities or LLM research agents, do not produce rampant overfitting. The explanation formalizes Occam's razor via a counting argument: successful ML strategies are highly compressible, so short descriptions lack room to memorize benchmark data and must capture real structure. LLM-based agents, being resettable and controllable, allow this hypothesis to be tested empirically.
Hacker News · security32
Event-Native Symbolic-Temporal Spike Encoding Framework for Heterogeneous Cyber Streams
An event-native spike encoding framework lets recurrent spiking neural networks run intrusion detection directly on heterogeneous packet and CAN bus streams.
The paper maps heterogeneous cyber events—categorical identifiers, local frequency context, and inter-event timing—directly into sparse spike-compatible inputs for spiking neural networks. This avoids flow aggregation and fixed windows that add buffering latency and obscure temporal structure in traditional IDS pipelines. Compact recurrent SNNs under edge-oriented neuromorphic hardware constraints achieve a hybrid anomaly metric of 0.987 on packet-level Network IDS and 0.980 on message-level CAN IDS.
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CounterPersona: Append-Only Defense Against Unauthorized Persona Skill Distillation
CounterPersona appends targeted counter-persona evidence after data collection to block AI systems from distilling an individual's behavioral patterns into reusable skills.
CounterPersona defends against unauthorized persona skill distillation, where attackers extract recurring patterns from collected personal data to replicate an individual's behavior. Unlike perturbation-based defenses that require modifying data before collection, it works in an append-only setting where historical records cannot be altered or revoked. It constructs targeted counter-persona evidence, packs compatible behavioral states into compact realization units, and strengthens them via rationale-guided consistency rewriting. Experiments show strong effectiveness across lexical, semantic, and LLM-based measures, remaining robust across different distillers.
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A Graph-Based Approach for Mapping Kernel-Level Telemetry to MITRE ATT&CK
Trace2ATT&CK maps eBPF kernel telemetry to MITRE ATT&CK via provenance graphs and RAG with local open-weights LLMs, validated on 347 Atomic Red Team tests.
Trace2ATT&CK collects kernel-level events via eBPF, correlates attacker commands into a provenance graph, and derives compact graph representations suitable for LLM-based reasoning, mapping behavior to MITRE ATT&CK techniques with ranked candidates and rationales. Mapping uses both pure LLM prompting and retrieval-augmented generation grounded in the ATT&CK knowledge base. It was evaluated on 347 Linux Atomic Red Team tests using locally deployed open-weights LLMs. RAG consistently improved ATT&CK mapping over pure prompting, and provenance graphs substantially outperformed raw telemetry, without compromising data confidentiality.
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I Am No One: Style-Aware Paraphrasing for Text Anonymization
Prompt-driven style-aware paraphrasing with LLMs cuts authorship attribution F1 by 60-70% while preserving content quality.
The paper proposes a style-aware, prompt-driven anonymization approach using pretrained LLMs to build compact stylistic profiles from minimal samples and rewrite text to suppress identifiable style markers while preserving meaning. It addresses stylometric re-identification risks in anonymized text, including ASR transcripts of meetings and call-center calls where leakage persists after acoustic anonymization. Across blog and review datasets, the approach reduces authorship attribution F1 by 60-70%, substantially outperforming both DP-based and non-DP baselines while maintaining readability.
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ZGCM-1: A Fully Open and Extremely Efficient Foundation Model for Math and Agentic Search
ZGCM-1 is a fully open 7B foundation model with 256K context that stays competitive with frontier models on math reasoning and agentic search.
ZGCM-1 is a fully open 7B dense foundation model trained from scratch using an efficiency-focused recipe: interleaved gated sliding-window and full attention, a stable FP8 Muon optimizer, and MDP-based mid-training with context scaling across 16K, 64K, and 256K. On mathematical reasoning and agentic search suites it remains competitive with much larger frontier models such as Qwen3-235B-A22B and GLM-5.1. The recipe yields a ~4.2x improvement in 16K pre-training time-to-loss, and all weights, checkpoints, training code, data recipes, and W&B logs are open-sourced.
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BlueSTAR: Tiered Agentic Architecture for Autonomous Cyber Defense
BlueSTAR is a tiered agentic LLM architecture for autonomous cyber defense, validated on live enterprise IT/OT cyber ranges against seven attack chains.
Researchers present BlueSTAR, a tiered agentic architecture for autonomous cyber defense in enterprise IT/OT networks that transforms high-volume security telemetry into compact indicators of compromise. It pairs deterministic containment for known threats with LLM reasoning for attacks requiring contextual and cross-cycle analysis, and introduces a resilience metric jointly weighing attacker reach, mission-critical impact, and defensive disruption. Evaluation on two live cyber ranges with seven attack chains based on real-world intrusion techniques covered credential theft, repeated compromise, concurrent attackers, and attacks on physical processes.
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The Missing Boundary: How Autonomous Agents Lose Control
Tencent research finds agents lose control in 55-62% of trajectories when degraded control boundaries coincide with executable unsafe opportunities across five models and 16 domains.
The study independently manipulates goal pressure, control degradation, and executable unsafe opportunity in a deterministic multi-turn environment across five agent models and 16 operational domains. Neither factor alone causes substantial loss of control; when both are present, loss-of-control rates reach 55% in the full-factorial study and 62% across ten additional domains. Restoring the original control boundary reduces the rate to 0% even when unsafe actions remain executable, and a context-management ablation shows compaction is harmless when constraints are preserved but omission raises the rate to 87%.
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Memory as Plans: World-Action Modeling with Memory-Grounded Planning
Researchers introduce MaP-WAM, decomposing memory-dependent robot manipulation into memory-grounded planning and plan-conditioned execution, achieving 83.3% on RMBench and 78% on real robots.
MaP-WAM converts long-term multimodal episodic memory — segment records with language instructions and sparse visual context — into compact plans of next-segment language goals and visual guidance. A World-Action-Progress model jointly predicts action chunks and execution progress, calibrating predictions via plan-observation alignment for adaptive segment transitions and closed-loop context updates. Structured attention keeps the executor context length fixed and enables key-value caching, yielding state-of-the-art 83.3% success on RMBench, 78.0% on real-robot tasks, and roughly constant inference latency as task history grows.
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Show-Harness: Just a VLM Agent Can Play Robots
Show-Harness lets VLM agents control robots via discrete semantic action units, outperforming VLA baselines zero-shot and after light fine-tuning.
Show-Harness is an embodied agent harness that exposes discrete semantic action units a VLM reasons over, with embodiment-specific interpreters grounding them into local robot actions. It enables zero-shot robot control with closed-source frontier VLMs and low-cost adaptation of small open-source VLMs using only a few GPU-hours of fine-tuning. The companion GUMI (GUI Manipulation Interface) extends the same semantic action space to GUI-based demonstration collection without specialized teleoperation hardware. Experiments show robust generalization across tasks, embodiments, and environments, beating representative agentic and VLA paradigms.
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Semigroup-JEPA: Latent Dynamics Consistency for Zero-Shot Physics Generalization
SG-JEPA world model conditions latent prediction on physical parameters, halving open-loop prediction error versus DINO-WM and boosting robotic control success.
SG-JEPA extends the LeWorldModel JEPA framework by supplying the governing physics parameter to the temporal model via action-conditioning and jointly training an encoder and predictor through autoregressive latent rollout. On out-of-distribution gravitational-field tasks it reduces open-loop prediction error by up to 2x versus DINO-WM on 2D datasets and increases 3D robotic control success rate up to 2.5x using independently trained diffusion policies. A linear feature analysis attributes most of the gain to the encoder learning features that the predictor can carry forward through rollout.
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