Search: “Adaptive Bridge”

Pelican-Sim 1.0: A General World Model Simulator for Embodied Intelligence

Pelican-Sim 1.0 predicts future observations from visual context and robot actions; four-step autoregressive rollouts yield 5.67x speedup and raise policy success from 70% to 93%.

Pelican-Sim 1.0 is a general world model simulator for embodied intelligence that predicts future observations from visual context and robot actions using a 28-dimensional unified action space valid across heterogeneous embodiments. Sparse mixture-of-experts layers reduce FVD by 6.530 versus the dense backbone, and causal adaptation with few-step distillation yields a four-step autoregressive simulator achieving a 5.67-fold speedup over the 35-step model. Trained on roughly one million real-world and simulated trajectories, PSNR improves over the strongest baselines by 4.636 on AgiBotWorld Beta, 2.080 on RoboMIND, and 10.343 on RoboTwin. Downstream on RoboTwin, adding 500 generated trajectories to 50 demonstrations per task raises policy success from 70% to 93%, and policy evaluation reaches a Pearson correlation of 0.994.

Hugging Face daily papers·Sep 9, 20:00 UTC · 6d agoAI research

ScienceBuddy: Recursive-in-Recursive Self-Improvement for Interactive Scientific Agents

ScienceBuddy couples harness evolution with model reinforcement learning so scientific agents continually self-improve from researcher feedback in an interactive workspace.

The authors release ScienceBuddy, an interactive scientific research workspace that transforms researcher requests, feedback, and execution evidence into tasks and evaluation rubrics for continual learning. Its recursive-in-recursive self-improvement paradigm couples harness evolution (inner recursion, model fixed) with model reinforcement learning under the improved harness (outer recursion). Case studies span four scientific task families, and the system is released to the scientific community as a research product.

arXiv cs.AI / cs.LG / cs.CL·Sep 15, 17:55 UTC · 20h agoAI research 2 sources

Enhancing Accessibility of Medical Texts through Large Language Model-Driven Plain Language Adaptation

Study shows LLMs with Mixture-of-Agents and QLoRA finetuning effectively simplify medical texts into plain language while preserving content.

The paper evaluates Plain Language Adaptation (PLA) using GPT-4o-mini, Gemini-1.5-pro, and LLaMA in zero-shot and few-shot settings. It compares prompting strategies, QLoRA finetuning across models, and integrates Mixture-of-Agents (MoA) techniques for robustness. Results demonstrate LLM-driven PLA makes healthcare texts more comprehensible while retaining essential content.

arXiv cs.AI / cs.LG / cs.CL·Sep 15, 16:33 UTC · 21h agoAI research

EVOHARNESSBENCH: Can Your Agents Keep Pace with an Evolving Harness?

Researchers introduce EVOHARNESSBENCH, a benchmark showing that evolving agent harnesses (tools, skills, agents) cause forgetting and inconsistent adaptation across 802 tasks.

The paper introduces EVOHARNESSBENCH, a benchmark that places non-stationarity in the externally supplied agent harness rather than in the task stream, evaluating agents across tools, skills, and specialist agents. It comprises 17 multi-stage harness streams built deterministically from verifier-based benchmarks, totaling 802 tasks, 520 tools, 42 skills, and 62 agents. Evaluation covers deployment (retention of previously accessible competence) and self-evolving adaptation settings. Results show harness expansion alone degrades previously solved tasks (harness-induced forgetting), adaptation gains are inconsistent, and retention and adaptation can pull in opposite directions.

Hugging Face daily papers·Sep 2, 20:00 UTC · 13d agoAI research

US fuel gauge exposure fell by more than half in three months

Help Net Security·Aug 7, 00:00 UTC · Aug 7, 2026Exploit / PoC

IntentFuzz: A Protocol-Aware Fuzzer for Automated Invariant Violation Detection in Intent-Based Cross-Chain Bridges

IntentFuzz protocol-aware fuzzer recovers bridge structure from unannotated Solidity and confirmed 22 invariant violations across 24 real-world deployments.

IntentFuzz formalizes a taxonomy separating invariant violations from settlement exposures in intent-based cross-chain bridges, then recovers a bridge's intent structure and deposit/fill function roles from unannotated Solidity source. It classified deposit and fill functions with 100% recall and 82% combined precision, and achieved 100% recall and precision on 23 planted-bug mutants. Across 24 real-world deployments it confirmed 17 genuine invariant violations with heuristic-only input generation, rising to 22 with its LLM-assisted tier, spanning eight vulnerable GitHub repositories with findings reproducible against public deployed bytecode.

arXiv cs.CR·Sep 11, 16:03 UTC · 4d agoResearch1

Bridging the Confidence Gap: Temperature Scaling for Calibrating Test-Time Prompt Tuning

CoTS temperature scaling cuts test-time prompt tuning's expected calibration error from 11.90% to 5.38% on ImageNet variants while raising accuracy.

The paper proposes CoTS, a post-hoc calibration method that applies temperature scaling to minimize the confidence gap between test-time-adapted and zero-shot predictions. A weak-strong ensemble variant, E-CoTS, further exploits multiple test-time augmentations to boost accuracy while maintaining calibration. E-CoTS reduces average expected calibration error from 11.90% to 5.38% on ImageNet variants while increasing accuracy from 60.74% to 62.95%.

arXiv cs.AI / cs.LG / cs.CL·Sep 15, 16:25 UTC · 22h agoAI research

Learning to Solve Hard Problems in RL for LLMs by Never Giving Up

Paper introduces Never Give Up adaptive sampling, fixing RL's 'Matthew Effect' where compute is wasted on easy problems and hard problems see little improvement.

Researchers identify a 'Matthew Effect' in reinforcement learning for LLMs, where RL yields large gains on easy problems but minimal improvement on hard ones because compute is misallocated. They propose Never Give Up (NGU), an adaptive sampling method that keeps generating samples for a problem until one is correct, using asynchronous RL to filter easy problems cheaply and concentrate compute on hard ones. NGU improves performance per compute on the Deepscaler math benchmark and iteratively solves the Manufactoria coding task where standard GRPO with per-test reward fails.

Hugging Face daily papers·Sep 10, 20:00 UTC · 5d agoAI research

Continue, Adapt, or Yield: In-Turn Adaptation to Overlapping Speech in Full-Duplex Agents

Duplex Cue evaluation shows PersonaPlex full-duplex agents adapt in-turn to listener contributions in only 34.8% of collaborative cases versus 68.2% for humans.

The paper introduces Duplex Cue, an evaluation of in-turn adaptation in full-duplex voice agents that separates listener intent (backchannel, collaboration, interruption) from speaker behavior (continue, adapt, yield). Using 208 scorable pairs from 300 human-confirmed cues in unscripted English conversations, it compares recorded human responses with PersonaPlex continuations generated while replaying listener audio. Humans adapt within the turn in 68.2% of collaborative pairs versus 34.8% for PersonaPlex, which otherwise continues unchanged (42.4%) or yields (22.7%).

arXiv cs.AI / cs.LG / cs.CL·Sep 11, 17:46 UTC · 4d agoAI research

AI for everyone in every language

Google says its AI now spans 300+ languages reaching 7 billion people, unveiling Gemini 3.5 Transcribe, Live Translate, and TranslateGemma models.

Google announced its technologies now support more than 300 languages spoken by 7 billion people, 86% of the global population. Gemini 3.5 Live Translate powers real-time spoken translation across 70 languages and 2,000+ language pairs, while Gemini 3.5 Transcribe is its most precise speech-to-text model. Its Universal Speech Model was trained on 12 million hours of audio using cross-lingual transfer learning, and TranslateGemma is a family of lightweight open translation models covering 55 languages that run on-device. Open-data partnerships include WAXAL covering 27 Sub-Saharan African languages and Project Vaani with 30,000+ hours of speech across 109 languages.

Google · AI·Sep 15, 16:00 UTC · 22h agoAI industry

Repeat-After-Me: Black-Box Adaptive Visual Prompt Injection

Researchers unveil Repeat-After-Me, a black-box visual prompt injection achieving over 80% success on Qwen3.6-27B and 47% on GPT-5.5.

Researchers present Repeat-After-Me, a black-box adaptive visual prompt injection that induces frontier VLMs to reveal PII or make malicious tool calls via injected images. It exceeds 80% attack success rate on Qwen3.6-27B and 47% on GPT-5.5 even when the benign user prompt is unrelated and does not authorize the injected task. In a real-world OpenClaw Discord deployment, a minimally injected image can overwrite TOOLS.md, enabling later remote code execution and secret exfiltration.

arXiv cs.CR·Sep 3, 22:44 UTC · 12d agoAI safety & security

Quantifying IIoT Sensor Node Criticality by Fusing its Data Criticality and Security Vulnerability

Researchers propose a Dempster–Shafer framework fusing IIoT sensor data criticality with CVSS 4.0/3.1 vulnerability scores to rank node criticality.

The paper introduces a framework that evaluates Industrial IoT sensor node criticality by fusing data criticality and cybersecurity vulnerability scores using Dempster–Shafer (D-S) theory. It was validated on a dataset from red wine production and is claimed to generalize to other industrial settings with minimal modification. Results show criticality rankings derived from CVSS 4.0 scores differ significantly from those derived from CVSS 3.1, underscoring how vulnerability scoring methodology affects security prioritization.

arXiv cs.CR·Sep 9, 07:03 UTC · 7d agoResearch

ModularRSI: Modular and Generalizable Recursive Harness Self-Improvement

Researchers propose ModularRSI, a modular benchmark-disjoint recursive self-improvement framework that evolves agent harnesses across five modules, improving TB2.0 and SWE-Bench Verified results.

ModularRSI targets generalizable recursive self-improvement (RSI) for agent harnesses by contrasting successful and failed trajectories for the same task and aggregating evidence across tasks to find recurring behavioral deficiencies. It decomposes the evolvable harness into five modules—Agent Loop, Tool Use, Observation Management, Context Management, and Task Completion Detection—each evolved independently within a restricted scope, then integrated with conflict resolution. Using 2,000 executable evolution tasks disjoint from evaluation benchmarks, it shows consistent gains on TB2.0 and SWE-Bench Verified and transfers across different foundation models.

Hugging Face daily papers·Sep 13, 20:00 UTC · 2d agoAI research

16 Typosquatted RubyGems Packages Steal Browser Credentials and Crypto Wallets

Typosquatted RubyGems packages deliver the StubMaker infostealer, stealing browser credentials, crypto wallets, and Telegram data from Windows developers.

OpenSourceMalware discovered a typosquatting campaign on August 15, 2026, tracked as StubMaker, publishing 16 malicious RubyGems packages (e.g., ubnuler, brumdler, brundlef) that imitate popular Ruby dependencies. The gems use the extconf.rb install hook to fetch a 22 MB Rust-based loader from GitHub, which launches a Go-based stealer ("wincfg") containing a DLL that bypasses Chromium's app-bound encryption to extract browser credentials, payment cards, crypto wallets and seed phrases, and Telegram Desktop data. Stolen data is uploaded as a password-protected ZIP to Gofile, with the download link sent over plain HTTP. The same actor also deployed 37 typosquatted npm packages delivering the identical loader and infostealer, and abused RubyGems' namespace reuse to republish malicious versions of yanked gems.

The Hacker News·Aug 21, 05:44 UTC · 26d agoMalware in the wild

Technical Manual for a Toolkit for Measuring Contextual Individuation in Transformer Language Models

An open methodology toolkit measures whether transformer language models contextualize fixed word forms across domains using bridge forms and layer-wise silhouette analysis.

The manual documents an open toolkit built around 'bridge forms' - identical written words recurring across two or more subject domains with a different sense in each - to test whether transformer language models individuate word occurrences by context beyond the embedding layer. It covers declarative specification of bridge forms, Wikipedia corpus acquisition, occurrence localization, layer-wise representation extraction, domain-pairwise silhouette measurement, and visualization, justifying each choice against failure modes such as sense contamination and subword-tokenization misalignment. It is a methodological and implementation reference and reports no empirical results.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 16:38 UTC · 11d agoAI research

Bridging the First-Hour Gap: Evaluating AI Reliability and Benchmarking Deficiencies in Cyber Incident Response for Law Enforcement

Survey of playbooks, LLMs, RAG, and agentic AI for law-enforcement cyber first responders finds RAG most viable but benchmarks inadequate for legal requirements.

The paper surveys decision-support architectures (playbooks, LLMs, RAG frameworks, agentic AI) for frontline law enforcement during the first hour of a cyber incident, where volatile digital artifacts risk procedural errors and evidence attrition. RAG-based systems are identified as a relatively viable intermediate solution, though prompt sensitivity and confident hallucinations in legal contexts pose major risks. The authors find current cybersecurity benchmarks insufficient for law enforcement safety and legal demands, and argue for a new benchmark focused on naive query robustness and evidence preservation.

arXiv cs.CR·Sep 11, 10:25 UTC · 5d agoResearch

A Princeton Researcher Proposes Recurrent Looped Transformer (RLT) that Carries Decoder State across Every Token, Fixing 96 Blocks per Token with Unbounded Temporal Depth

Princeton researcher Yifan Zhang proposes Recurrent Looped Transformer, carrying full decoder state across every token for unbounded temporal depth.

Yifan Zhang's technical report defines the Recurrent Looped Transformer (RLT), pairing a causal encoder with a recurrent decoder whose final output and layerwise sliding-window attention cache carry into every subsequent token with no prompt-response boundary reset. The reference configuration ties 48 encoder and 48 decoder layers, executing 96 logical blocks per token while the state path grows to 48t blocks after t tokens at fixed per-token compute. The report details RL replay contracts that rebuild all states under current parameters and exact prefix snapshots for multi-turn serving, but explicitly reports no measured efficiency, reasoning quality, or scaling results.

MarkTechPost·Sep 13, 17:02 UTC · 2d agoAI research1

NVIDIA Brings Real-Time AI to Broadcast, Sports and Global Streaming at IBC

NVIDIA expanded its AI for Media suite at IBC 2026, adding NIM microservices for synthetic video detection, body pose, frame generation, upscaling and HDR.

At IBC 2026 in Amsterdam, NVIDIA announced a major expansion of NVIDIA AI for Media, a collection of GPU-accelerated SDKs, NIM microservices and blueprints for broadcast and streaming workflows. The Synthetic Video Detector (SVD) NIM microservice reaches 99.3% accuracy on text-to-video and 97.7% on image-to-video content, while Video Frame Generation boosts frame rates 2x-4x and Video Super Resolution adds 10-bit support; TrueHDR converts SDR to HDR at up to roughly 2,000 nits. Partners including Dalet, TwelveLabs, Wowza, Vizrt and Ross Video are integrating the new services into verification, compliance and live-production workflows.

NVIDIA Blog·Sep 9, 16:00 UTC · 6d agoAI industry

Top 10 Best Browser Isolation Solutions in 2026

A 2026 market overview ranks ten remote browser isolation tools, with Menlo Security as the pure-play reference as SSE vendors bundle isolation.

The article compares ten remote browser isolation (RBI) options, including Menlo Security, Zscaler, Cloudflare, Palo Alto Networks, Broadcom (Symantec), Forcepoint, Skyhigh Security, Ericom (Cradlepoint), Authentic8, and Garrison. It argues that RBI has become a bundled policy action inside SSE platforms from Zscaler, Cloudflare, Palo Alto, Broadcom, Forcepoint, and Skyhigh, compressing standalone pricing and driving consolidation such as Ericom's isolation moving under Cradlepoint (Ericsson). Enterprise browsers like Island and Chrome Enterprise Premium are reshaping the RBI-versus-browser decision for managed users, while selective policy-driven isolation of risky categories is described as the prevailing 2026 architecture. The piece is a buyer's guide with vendor positioning, not an incident or vulnerability report.

Cyber Security News·Sep 11, 07:01 UTC · 5d agoIndustry

Biology-in-the-loop: Amortized Adaptive Hit Discovery in CRISPR Screens

Researchers release AssayBench-Loop, a 1,389-screen CRISPR benchmark, and AssayLoop, a framework that learns adaptive hit discovery policies.

The paper introduces AssayBench-Loop, a large-scale benchmark of 1,389 CRISPR screens across five phenotype categories for adaptive hit discovery under budget constraints. It also introduces AssayLoop, which combines AssayFormer, a transformer-based amortized acquisition policy trained across historical screens, with LLM-derived biological priors via an adaptive handoff. On temporally held-out screens, AssayLoop achieves 5.67-fold enrichment over random selection and recovers 27.7% of hits after assaying roughly 5% of the candidate library, outperforming existing adaptive-design methods and standalone LLMs.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:45 UTC · 5d agoAI research1

Researchers Report 84 Flaws in 4G and 5G Cores, Including a Session Hijacking Flaw

The Hacker News·Aug 3, 08:23 UTC · Aug 3, 2026Exploit / PoCCVE-2026-8233CVE-2026-36884CVE-2025-66776

Active Adaptation, Not Static Defense: Temporal Dynamics of Preventative Steering in Adversarial Fine-Tuning

Researchers analyze why Preventative Steering protects LLMs against malicious fine-tuning, finding active adaptation drives protection, and propose Progressive Intensity Scheduling.

The paper studies Preventative Steering, a training-time defense that injects undesirable-trait persona vectors during adversarial fine-tuning and removes them at evaluation time. Temporal analysis shows protection emerges from an early compensatory adaptation phase followed by a steady-state phase, with attention output projections acting as the dominant residual-write route for defensive updates. Intervention Delta Preservation experiments show that preserving or reinjecting weight offsets fails to maintain protection, indicating reliance on active adaptation rather than a static defense. The proposed Progressive Intensity Scheduling improves safety robustness on Qwen2.5 and Gemma-3 while reducing harmful trait expression.

arXiv cs.CR·Sep 9, 13:19 UTC · 7d agoAI safety & security1

National Life Group CISO expects more vulnerabilities in six months than in thirty years

National Life Group CISO Becky Palmer says agentic AI resolves four of five SOC investigations and urges AI-speed patching practices.

In a Help Net Security interview, National Life Group CISO Becky Palmer argues frontier AI will uncover more vulnerabilities in the next six months than in the last thirty years, compressing time from disclosure to weaponized exploit from weeks to hours. She reports agentic AI in her SOC resolves 4 of 5 investigations without human escalation, saving hours daily on enrichment and summarization. She also details compensating controls such as virtual patching, least-privilege restrictions, and heightened monitoring, plus procurement questions to separate working AI products from wrappers.

Help Net Security·Sep 2, 00:00 UTC · 14d agoIndustry

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·Sep 15, 17:42 UTC · 20h agoAI research

AI is exposing a security structure built for yesterday’s threats

EY's Jeffrey Sallet argues AI-driven deepfakes and impersonation require integrating cybersecurity, physical security, HR and legal functions.

The opinion piece contends AI-powered impersonation, deepfakes and automated social engineering cross digital, physical and operational boundaries that siloed security programs cannot cover. It cites an EY survey of 250 corporate leaders where only 12% feel most prepared to detect a targeted physical attack, and describes transnational groups using deepfakes and stolen identities to bypass virtual HR hiring loops. The author urges unified cross-functional verification pipelines and shared threat intelligence between CISOs and chief security officers.

CSO Online·Sep 15, 09:00 UTC · 1d agoIndustry

Large Language Models Develop Novel Social Biases Through Adaptive Exploration

An OpenReview paper reports that large language models can develop novel social biases through adaptive exploration behavior.

The paper, hosted on OpenReview, examines how adaptive exploration during language model learning or interaction can give rise to social biases that were not explicitly present in training data. It surfaced on Hacker News with 25 points and 4 comments, indicating limited community discussion. The findings are relevant to fairness auditing and behavioral evaluation of deployed LLMs.

Hacker News · security·Sep 8, 21:47 UTC · 7d agoAI researchHN 25↑ · 4 comments

New SLEEPWALKER Backdoor Waits for One Crafted Packet, Then Runs Its Own Bytecode

A new Windows backdoor dubbed SLEEPWALKER hides as ESET Management Agent's dpapi.dll, waits for a crafted network packet, then executes custom 23-instruction bytecode.

Researcher Dominik Reichel documented SLEEPWALKER, an unsigned 59,904-byte 64-bit Windows DLL that side-loads into ESET Management Agent's ERAAgent.exe while impersonating dpapi.dll. The implant monitors every network interface indefinitely for a specifically crafted trigger packet, then executes commands written in a custom 23-instruction bytecode language over six transports, including TCP, UDP, ICMP, SMB named pipes, raw promiscuous capture, and VMware VMCI. It has no embedded infrastructure, makes no outbound connections of its own, and requires prior local administrator access to install; no victims, attribution, or in-the-wild deployment are confirmed. Reichel assesses the design as consistent with a targeted, well-resourced operation, while ESET calls the scenario's security relevance negligible since it provides no new access path.

The Hacker News·Sep 4, 04:00 UTC · 12d agoMalware

Domain-Incremental Learning for Multi-Channel Replay Speech Detection

First continual learning benchmark for multi-channel replay speech detection shows task-specific beamforming cuts catastrophic forgetting across 24 acoustic environments.

Researchers frame replay-attack detection for voice-controlled systems as domain-incremental learning over acoustic environments, evaluating a beamformer-based detector across all 24 environment orderings of the ReMASC corpus with five seeds. Naive sequential fine-tuning raises error rates on previously learned environments by 18.8 points, while elastic weight consolidation halves forgetting but loses plasticity and gradient projection memory is statistically indistinguishable from naive fine-tuning. A task-specific beamformer keeping one spatial front-end per environment significantly improves final and incremental accuracy, and the last environment in a sequence dominates final performance.

arXiv cs.CR·Sep 10, 08:03 UTC · 6d agoResearch1

When AI Remembers Too Much

Unit 42 PoC shows indirect prompt injection can poison Amazon Bedrock Agent long-term memory, enabling silent exfiltration of conversation history across future sessions.

Palo Alto Networks Unit 42 published a proof of concept showing that indirect prompt injection can silently poison the long-term memory of Amazon Bedrock Agents when the memory feature is enabled. Malicious content on a webpage or document manipulates the agent's session summarization process, so injected instructions persist across sessions and are added to later orchestration prompts, silently exfiltrating user conversation history. The issue is not a vulnerability in the Amazon Bedrock platform but an illustration of the broader unsolved LLM prompt-injection challenge. Amazon reviewed the research and stated that Bedrock Guardrails with the prompt-attack policy provides effective mitigation.

Palo Alto Unit 42·Aug 17, 12:32 UTC · Aug 17, 2026AI safety & security

Drift-Constrained Optimization: Only Direction Matters in Fine-Tuning Instruct Models

Drift-Constrained Optimization reformulates fine-tuning as update-direction selection, letting Qwen3 models improve target tasks within a behavioral drift budget.

The paper specifies a behavioral drift budget before optimization and shows that update direction is the remaining degree of freedom, reformulating fine-tuning as a direction-selection problem. In a stringent QA-only setting where instruct models must still generate multi-step reasoning at inference, a coarse layer-selective probe reverses the failure of QA-only fine-tuning. Across Qwen3-8B and Qwen3-14B, these directions substantially improve scientific reasoning and multilingual translation, matching or outperforming dedicated translation systems over 100+ languages and giving stronger initialization for reinforcement learning.

Hugging Face daily papers·Sep 11, 20:00 UTC · 4d agoAI research

China-Based Artificial Intelligence Companies Conducting Industrial-Scale Distillation Campaigns Against U.S. AI Companies

NSA, CISA, and FBI warn DeepSeek, Alibaba, and other Chinese AI firms ran industrial-scale distillation of U.S. frontier models, threatening U.S. AI leadership.

A joint NSA, CISA, and FBI Cybersecurity Advisory (AA26-251A) says China-based firms DeepSeek, Moonshot AI, Alibaba, MiniMax, StepFun, and Z.AI extracted billions of tokens from U.S. frontier models including Claude, GPT, Gemini, and Grok, likely with Chinese government knowledge. Campaigns running since at least late 2024 used native APIs, cloud providers, third-party aggregators, gray-market proxy "transfer stations", and shared premium subscriptions to bypass geographic restrictions, evade safeguards, and violate providers' terms of use. The agencies recommend detecting anomalous prompts, accounts, and usage patterns; subtly altering responses to suspected distillers; and cross-organization intelligence sharing. They also call DeepSeek's publicly cited $5.6M training cost misleading because it excludes data acquired through distillation.

CISA Advisories·Sep 8, 12:00 UTC · 8d agoAdvisory in the wild1

Train Smarter, Not Harder: Switching Signal-Guided Training in Active Learning

HybridAL is an active-learning training schedule that switches from retraining to fine-tuning on stabilization signals, saving up to 49% time.

Researchers find that choosing between retraining from scratch and fine-tuning is an exploitable decision variable in active learning: retraining helps in early rounds while fine-tuning is safer once the model trajectory stabilizes. HybridAL monitors an online stabilization signal using spectral exponent change and accuracy change, switching from retraining to fine-tuning after sustained stabilization. Across three encoder backbones and six text-classification tasks with five seeds each, HybridAL keeps endpoint macro-F1 non-inferior within a 0.010 margin, saves up to 49% of retraining time, and improves the time-calibration trade-off measured by negative log-likelihood.

Hugging Face daily papers·Sep 5, 20:00 UTC · 10d agoAI research

Cybersecurity jobs available right now: March 10, 2026

Help Net Security's roundup lists open cybersecurity roles at BioNTech, AIG, ServiceNow and others across Europe, the Middle East and Canada.

A job-board roundup of cybersecurity openings including Associate Director Application Security at BioNTech (Germany), CISO at AIG (Israel), Cloud Security Professional at ServiceNow (Italy), and SOC/GRC, analyst, engineer and data governance roles in the UK, UAE, India, Canada and France. Roles span application security, cloud security, SOC operations, compliance and OT environments. Most listings are marked no longer accepting applications.

Help Net Security·Aug 28, 09:06 UTC · 19d agoIndustry

UniMate: One Unified Model to Animate Diverse Skeletons

Researchers introduce UniMate, a topology-aware diffusion transformer generating text-driven motion for arbitrary 3D skeletons without per-skeleton retraining.

UniMate is a unified foundation model that synthesizes articulated motion for arbitrary skeletons from a rigged 3D asset and a text prompt, with no test-time optimization or per-skeleton fine-tuning. It uses a topology-aware diffusion transformer combining graph-aware attention bias, a spectral rotary position embedding generalizing RoPE via the graph Laplacian, and a rest-pose topological conditioner. Trained on UniML3D, a curated set of 13,006 motion sequences spanning bipedal to serpentine skeletons, it outperforms state-of-the-art baselines and supports zero-shot cross-topology transfer, in-betweening, and text-guided editing.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 17:59 UTC · 11d agoAI research

Environments as Scaffold: Enriching Feedback to Bootstrap Self-Evolving Agents in Long-Horizon Tasks

Researchers propose Feedback-Enriched Environments (FEEs) that reduce reward sparsity and improve RL training of Qwen3-based agents on SciWorld and BFCL.

The paper proposes shifting from agent-side warmup (SFT) to environment-side adaptation via Feedback-Enriched Environments to address severe reward sparsity in RL training of long-horizon LLM agents. A pilot study defines a feedback strategy that transitions from action guidance to observation enrichment in later training stages. Large-scale experiments on SciWorld and BFCL across Qwen3 model scales and GRPO, GSPO, and DAPO show consistent gains, plus stabilized training dynamics and proactive exploration.

Hugging Face daily papers·Sep 7, 20:00 UTC · 8d agoAI research

Decoy Direction Optimization: A Post-Hoc Defense Against LLM Abliteration

Researchers introduce Decoy Direction Optimization, a cheap weight-editing defense that blinds refusal-direction ablation attacks against open-weight LLM safety guardrails.

Refusal Feature Ablation bypasses safety guardrails in open-weight LLMs by projecting out a linear refusal direction, often with high attack success rates. Decoy Direction Optimization injects a high-magnitude nonlinear decoy into MLP neurons so attackers' contrastive estimators ablate a harmless orthogonal feature instead. Evaluated across six model families, DDO keeps ASR below 10% under standard RFA and on Llama-3-8B-Instruct reduces Heretic weight-level attack ASR from 88.7% to 18%. It costs 30 to 450 times less per configuration than trained defense baselines.

Hugging Face daily papers·Sep 13, 20:00 UTC · 2d agoAI safety & security

Co-Evolving Harnesses and Models: On-Policy Correction Helps Weaker Models Catch Up Where Imitation Fails

Research shows on-policy expert correction, not imitation fine-tuning, lets weaker agent models catch up under evolved harnesses.

Researchers study how to combine automated agent-harness evolution with lightweight fine-tuning across seven enterprise agent tasks. Naively training weaker models (Qwen3-Coder, Gemma 4) on expert trajectories under an evolved harness regressed performance by 4 to 30 points on all tasks. They propose an on-policy correction pipeline, automated by a meta-level MLE agent, where an expert rewrites only the failing turn of the weaker model's rollout, preserving model-harness fit.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:53 UTC · 7d agoAI research

The Evolution of the Agent Harness

Latent Space essay argues late-2025 agent gains came from models and harnesses maturing together, with harness logic absorbed into model weights.

The piece defines the agent harness as everything beyond model weights—tools, context, memory, guardrails—and charts its evolution from ReAct prompting (October 2022) through AutoGPT's premature autonomy, Cursor/Copilot's human-in-the-loop retreat, and Devin's roughly 15% success rate, to o1's capability overhang and Claude Code's February 2025 terminal agent with permission rules. It argues the Christmas 2025 jump cited by Transformer co-inventor Lukasz Kaiser reflected model and harness curves crossing, and that remaining harnesses will serve human attention rather than the model.

Latent Space·Aug 22, 07:30 UTC · 25d agoAI tools & infra

Safety for Whom? Boundary-Aware Self-Distillation for Controlled LLM Safety Refusal

A self-distillation safety framework tunes narrow-boundary refusals in Qwen3-8B, raising target-domain refusal to 84.75% while cutting over-refusal from 15.20% to 5.20%.

The paper formulates narrow-boundary safety, where deployments need refusals within specific topics rather than whole subjects, and proposes an offline self-generated framework with controlled topic generation, escalating retries, and harmful-benign boundary pairs. On political persuasion with Qwen3-8B, the method raised target-domain refusal from 9.47% to 84.75% and cut the mean unsafe-response rate across three broader benchmarks from 26.26% to 0.14%. Verified target-model responses reduced over-refusal from 15.20% to 5.20%, and boundary-pair data cut comply-side over-refusal on held-out pairs from 32.94% to 4.16%. Results show data composition controls the safety-usability trade-off and alignment should be evaluated on both sides of the refusal boundary.

Hugging Face daily papers·Sep 2, 20:00 UTC · 13d agoAI safety & security1

Model-Aware Schedules Improve Generation via Fiberwise Optimal Transport

Researchers introduce model-aware diffusion schedules via fiberwise optimal transport, cutting flow-matching FID on CIFAR-10 by 38.6% at 16 function evaluations.

The paper proposes constructing diffusion and flow-matching sampling schedules from a fiberwise prediction risk defined via optimal transport, combined with coefficient-path kinetic action, yielding a closed-form time allocation. Across DDPM and flow-matching experiments spanning targets, datasets, and architectures, the schedules beat model-agnostic baselines, including a 38.6% relative FID reduction for flow matching on CIFAR-10 at 16 function evaluations. Normalized fiberwise-risk profiles from independently trained models align closely, suggesting empirical universality, and a frozen analytic allocation template retains most of the gains.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:30 UTC · 5d agoAI research