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Search: “corruption”

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DomainAllCyberAIWindow24h3d7d30dAllIn the wildImportant onlyShow duplicates
CategoryAllVulnerability 984Exploit / PoC 449Ransomware 345Malware 330Policy & legal 165Threat actor 119Phishing & fraud 117Data breach 109Industry 68Research 34Advisory 23AI safety & security 23AI research 9AI industry 5
SeverityAllcritical 1330high 702medium 416low 286info 59

Quantile-based Loss Filtering for Outlier-Robust Stochastic Gradient Descent

Quantile-k-Loss SGD filters corrupted component losses by quantile sampling, proving linear convergence while outperforming standard and min-k-loss SGD.

The paper proposes Quantile-k-Loss SGD (Q(k)L-SGD), a loss-filtering framework for finite-sum optimization with corrupted components that samples k losses per iteration and updates using an index from the lower empirical q-quantile. The authors prove linear convergence under standard convexity, requiring sample size to scale with the number of corruptions, plus a complementary small-sample probabilistic analysis. Experiments on polynomial regression, regularized logistic regression, and hinge loss show intermediate quantiles often outperform both standard SGD and min-k-loss SGD.

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

When Models Edit Too Much: On the Fidelity of Minimal Code Edits

A 400-task BigCodeBench evaluation shows frontier LLMs widely over-edit code; a preservation instruction cuts excess edits and raises Pass@1 by 2.3 points.

Researchers built an evaluation framework from 400 BigCodeBench problems with injected AST-level corruptions, each with a known minimal patch, to measure over-editing in LLM code repair. Even strong models like GPT-5.5 produce unnecessarily large edits despite high Pass@1. Adding a preservation instruction reduced average excess Levenshtein distance from 0.195 to 0.131, cut added cognitive complexity by 26.6%, and raised Pass@1 by 2.3 points. Reinforcement learning post-training gave the best out-of-domain edit-fidelity trade-off, while supervised fine-tuning overfit to seen corruption patterns.

Hugging Face daily papers · 14d agoAI research1

Long-Lived Characters, Local Inference: Incremental Memory Maintenance for Game NPCs

Researchers present incremental KV-cache memory maintenance for long-lived game NPCs running locally on a quantized Qwen hybrid model.

The paper studies incremental memory maintenance for long-lived game NPCs deployed locally with a quantized Qwen hybrid recurrent-attention language model. The runtime removes superseded attention KV entries, computes replacement records at the true sequence tail, and preserves the continuing recurrent state and unchanged KV. Experiments across eight scripted maintenance rounds show true-tail updates preserve current-state and historical bindings, while slot-preserving alternatives repeat a double-subtraction error.

arXiv cs.AI / cs.LG / cs.CL · 13h agoAI research

Feature Recovery for Object Understanding After Irreversible Fire Damage

TRACE benchmark with 21.4K scenes studies post-fire object understanding; a Feature Recovery Module improves degraded-image retrieval by 12.5% and material recovery by 20.1%.

The paper introduces TRACE, a transformation-aware benchmark with 21.4K real-image-grounded synthetic scenes, 499 object identities across 189 categories, and five tasks covering degraded-object detection, pristine-state recovery, material recovery, description generation, and functional reasoning. Existing models degrade sharply: RF-DETR mAP falls 71% relative from least to most severe level, and InternVL3.5 retrieval R@1 drops from 93.85 to 28.11. The proposed Feature Recovery Module maps degraded encoder features to pristine-aligned representations while keeping the host model frozen, averaging relative gains of 12.5% for retrieval and 20.1% for material recovery across VLM hosts and severity levels.

Hugging Face daily papers · 7d agoAI research

H Company Releases NeoMME: A Family of 260M and 800M Single-Tower Multimodal Encoders That Drop the Vision Tower and Causal Decoder

H Company released NeoMME, 260M/800M single-tower multimodal encoders matching 3.75B ColQwen2.5 on ViDoRe v3 while being 14.4x smaller, under Apache 2.0.

H Company released NeoMME, a family of 262,937,906- and 793,715,032-parameter bidirectional encoders that process text and raw 32x32 image patches in a single tower, pretrained via masked diffusion and released under Apache 2.0 with day-zero Hugging Face Transformers support. NeoMME-Retriever-260M reaches 0.523 nDCG@10 on ViDoRe v3, matching 3.75B-parameter ColQwen2.5 while being 14.4x smaller; the 800M model scores 0.556. Hierarchical token pooling with int8 and binary quantization shrinks late-interaction indexes from roughly 1.5 MB to 6 kB per page while retaining 95.19% of nDCG@10; text-only BEIR retrieval remains a weak spot.

MarkTechPost · 10d agoAI research

ENEAS: Embedding-guided Neural Ensemble for Adaptive Segmentation

ENEAS adds text prompting and semantic verification to video segmentation to keep tracking targets through occlusion and reject lookalike distractors.

ENEAS is a unified text-promptable method for instance tracking and open-concept semantic discovery in video, designed to fix temporal hallucinations, spatial fragmentation, and semantic misclassification seen in SAM 3-class foundation models. It extends the geometrically robust SeC architecture with a text-prompting adapter and temporal memory, and uses a verification layer combining fast visual embedding matching with conditional VLM refinement for ambiguous candidates. It targets 3D reconstruction pipelines where a single misclassified distractor corrupts the asset. Code and models are open-sourced.

Hugging Face daily papers · 14d agoAI research

Scaling Automatic Research Agents via World Models

WMRL replaces environment execution with a world model in RL, accelerating research-agent post-training 3-4x and letting 4B/9B agents beat 48B/120B open-weight agents.

The paper identifies that environment execution dominates RL training cost for automatic research agents because each execution occupies an exclusive sandbox while generation batches efficiently. World Model RL (WMRL) substitutes a learned world model for execution, with Online Debiasing and Inverse-Variance Denoising to handle reward bias and noise, and the paper proves both improve convergence guarantees. WMRL accelerates training 3-4x across tasks and outperforms standard RL baselines; post-trained 4B and 9B agents beat 48B and 120B open-weight agents on held-out benchmarks. WMRL also transfers to post-training embodied VLA policies.

Hugging Face daily papers · 19d agoAI research1