Smart search ranks by meaning as well as keywords (one row per story, last 45 days).
Ubuntu USN-8751-1 patches Urwid weak PRNG issue allowing local attacker possible denial of service or code execution.
Katriel Moses discovered that Urwid used a weak pseudo-random number generator. A local attacker could potentially exploit this issue to cause a denial of service or execute arbitrary code. Ubuntu has released a patch under USN-8751-1.
New HOPE method uses second-order objectives to prune Mixture-of-Experts LLMs, outperforming REAP at 50% pruning on models up to 122B parameters.
Researchers derive HOPE (Higher-Order Pruning of Experts), a second-order pruning objective for Mixture-of-Experts language models that provably minimizes an upper bound on pruning error by modeling cooperative expert interactions. They show REAP, a state-of-the-art first-order method, is a special case of HOPE with interaction terms ignored. Across three frontier MoE models up to 122B parameters, two calibration sets, and benchmarks covering math, instruction following, coding, and agentic tasks, HOPE achieves the best average rank (1.58 of 5 at 50% pruning versus 2.42 for REAP), with gains up to +6.1% on agentic coding.
CISA advisory details six flaws, aggregate CVSS 9.6, giving attackers full control of Digital Watchdog VMAX DVR/NVR surveillance devices.
CISA advisory ICSA-26-258-01 discloses six vulnerabilities affecting all versions of Digital Watchdog VMAX A1 G4 DVRs, VMAX IP G4 NVRs, VMAX A1 PLUS, VA1G4, and VG4 recorders, with aggregate CVSS v3 of 9.6. Flaws include an authentication bypass leaking plaintext admin credentials (CVE-2026-68953), hard-coded credentials enabling root FTP access (CVE-2026-66890, CVE-2026-68950), root command execution (CVE-2026-68070), missing authorization on state-changing CGIs (CVE-2026-66887), and predictable session tokens (CVE-2026-66372). Successful exploitation grants full administrative control, live and recorded surveillance access, and a network pivot point. Products are deployed worldwide across commercial facilities, government, healthcare, and transportation sectors.
Pruning study across four LLM architectures finds dense models degrade sharply on smart-home tool calling while MoE models tolerate far more.
Researchers systematically study pruning-induced degradation in smart-home tool calling across four LLMs spanning dense Transformer, dense hybrid, and mixture-of-experts architectures, combining depth, width, hybrid, and expert pruning methods, and evaluate over 19,500 instances from three datasets after post-pruning supervised fine-tuning. Dense models show narrow safe pruning regions followed by sharp degradation, while MoE models tolerate substantially more pruning. Pruning degrades grounded specificity (operation, device, argument, value) before schema-level intent, and aggressive dense pruning can induce systematic over-refusal.
BXT framework mitigates FHE memory bottlenecks via ciphertext compression, serialization, delayed seeding, and digit pruning, achieving up to 3.8x CNN inference speedup.
A new paper proposes BXT, an optimization framework for word-wise Universal Fully Homomorphic Encryption that targets the memory bottleneck rather than compute. It combines four techniques: ciphertext compression via seed regeneration, bit-packed ciphertext serialization for L2-to-L1 transfers, delayed PRNG-heavy offline seed generation across aggregated operations, and fault-aware ciphertext digit pruning. On CNN inference, the BXT-CSO50 configuration achieves up to 3.8x speedup over a 100x GPU baseline with under 1% accuracy loss at 50% comparison precision.
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%).
Community fine-tune Qwopus3.8-27B-Flash, built on Qwen3.8-27B, cuts agent reasoning latency with 12.8% faster decoding and 80.7% MTP acceptance.
Jackrong released Qwopus3.8-27B-Flash, a fine-tune of Qwen3.8-27B optimized for long-running agent workloads, reporting 12.8% faster decoding and 80.7% multi-token-prediction acceptance. Training used roughly 1.5 million teacher-scored SFT examples filtered to the top 10%, followed by reinforcement training with NVIDIA NeMo-RL and GSPO. The author notes an explicit trade-off: MMLU-Pro mixed-set scores are lower than the base model, and a known bug can produce incorrect Python indentation. Author-provided benchmarks have not been independently verified.
Systematic study finds model pruning causes frequency-dependent long-tail forgetting in medical imaging and that gradient-informed methods best preserve explanations.
Across two long-tailed medical imaging datasets, two CNN architectures, four pruning methods, and sparsity up to 95%, the study measures predictive performance, explanation stability, and faithfulness. Rare classes degrade earlier and more severely than frequent ones, while explanation reliability depends mainly on the pruning strategy, with gradient-informed methods degrading least. Mechanistic analysis ties explanation collapse to loss of class-discriminative gradients rather than vanishing feature activations, recommending class- and explanation-aware evaluation of compression.
New tokeniser study shows search procedure, not optimisation objective, drives bits-per-byte performance across model sizes, vocabulary sizes, and multilingual settings.
The paper disentangles BPE and UnigramLM along two axes: optimisation objective (compression vs log-likelihood) and search procedure (bottom-up merging vs top-down pruning). Two new algorithms, BottomUpLL and TopDownComp, complete the 2x2 design space, and trained language models are evaluated on bits-per-byte and BLiMP across model sizes, vocabulary sizes, and English-only vs multilingual domains. Bottom-up tokenisers consistently achieve lower bits-per-byte in most settings, while BLiMP shows no consistent relationship with design choice.
Study shows visually grounded token embeddings in a small masked LM persist through training and improve object-property knowledge, but escape standard BabyLM benchmarks.
The paper implements ostensive definition for a small DeBERTa masked language model trained on 10M words, seeding visually grounded tokens with embeddings derived from labeled image regions before training. Visual initialization leaves a persistent, seed-replicated advantage on object-property knowledge (COMPS) and a corpus-tailored Visual-Property Swap benchmark covering color, material, size, and shape, but has no effect on most BabyLM grammar benchmarks. Synthetic grounding of previously unseeded words causally transfers the advantage to exactly those words.
A study of 180 US workers found each LLM phishing personalization level raised click-intention odds by 28%, but credibility depends on context fit.
The arXiv paper evaluates how personalized pretexts in LLM-generated spear phishing affect perceived credibility, using 180 US working adults across 1,436 evaluations of emails with four cumulative personalization levels, from workplace context to shared-project details. Convincingness rose 2.40 points per level in sensitivity analysis and click-intention odds increased 28% per level, while non-clickers shifted toward deleting rather than reporting. Qualitative coding showed details matching the recipient's role and routines supported credibility, whereas incorrect, vague, or channel-inappropriate details raised suspicion. The authors argue personalization effectiveness depends on pretext fit, with implications for workplace security training.
TCG issued guidance to help businesses verify that trusted platform modules genuinely meet quantum-safe requirements.
The Trusted Computing Group (TCG) published new guidance aimed at proving that trusted platform modules (TPMs) genuinely satisfy essential quantum-safe requirements. The document gives businesses a way to validate vendor claims about post-quantum readiness in hardware security rather than trusting marketing assertions. This is a guidance publication, not an incident or vulnerability disclosure.
Trusted Computing Group published PTP 1.07-based requirements defining PQC-ready and PQC-upgradable TPM designations to guide buyers assessing quantum-safe hardware.
The Trusted Computing Group published requirements establishing PTP 1.07, based on TPM 2.0 Library Specification Version 1.85, as the baseline for a 'TCG PQC-ready TPM', and defined a second designation, 'TCG PQC-upgradable TPM', for chips that can be upgraded to that support. The guidance lets buyers request vendor evidence that TPMs meet end-to-end post-quantum requirements for identities, attestation, and hardware-anchored trust rather than single-algorithm compliance. TCG also announced plans to extend its certification programs to certify TPMs meeting PTP 1.07, noting that 90% of businesses still lack a formal PQC roadmap.
French BabyLM entry METRON-FR (125M GPT-2, 92.47M words) shows tokenizer artifacts dominate child-scale zero-shot evaluation; proposes standard diagnostics.
METRON-FR is a 125M-parameter GPT-2 pretrained on 92.47M French words, submitted to the BabyLM 2026 Strict track, scoring 85.97% on the native Quebec-French QFrBLiMP benchmark and 62.80% on the BabyLM-weighted leaderboard. A cross-lingual GLUE protocol combining French task-data translation with rank-16 LoRA shows relational tasks gain while world-knowledge tasks regress. Bilingual Lexicon Induction reaches p@1 of 68.84%, 18x above chance, and ablations show single-token zero-shot scoring is dominated by tokenizer and template artifacts at child scale.
Qwen released Qwen3.8-Flash-Next, an experimental 125B-parameter open-weight MoE previewing the Qwen4 architecture, with Unsloth shipping optimized GGUF quants.
Qwen released Qwen3.8-Flash-Next, an experimental open-weight preview of the architecture planned to underpin Qwen4. The model has 125B parameters with 6B activated, 512 experts (10 routed plus 1 shared), Qwen Sparse Attention (QSA), Gated DeltaNet, Gated Residual, and n-gram embeddings, with 262,144-token native context extendable to 1,000,000 tokens. Unsloth provides Dynamic 3.0 GGUF quantizations, and multi-token prediction (MTP) delivers 1.3-1.7x faster inference via llama.cpp or Unsloth Desktop.
A unified ViT compression pipeline (H-BAC pruning, quantization, distillation) cuts plant-disease models 54.5x to 6.01 MB while keeping 95.13% accuracy.
Researchers combined Hessian-Balanced Adaptive Block Pruning (H-BAC), guided by second-order sensitivity estimation, with quantization and attention-based knowledge distillation to compress Vision Transformers for on-device chilli plant disease detection in India. On a 3-class cross-village, cross-device out-of-distribution dataset, the integrated pipeline reduced model size from 327.42 MB to 6.01 MB (54.5x) at 95.13 +/- 2.32% accuracy, matching the 95.13% FP32 baseline. Ablations also show a directly trained 6.01 MB INT8 student reaches 94.87% accuracy, indicating where pruning and distillation add limited value.
A widely shared Hacker News gist claims Qwen 3.8 reproduces GPT-5.5 Pro's reasoning prefill behavior.
A gist circulating on Hacker News claims the Qwen 3.8 model follows the same reasoning-prefill patterns observed in GPT-5.5 Pro. The post drew 41 points and 8 comments, but no technical details are included in the available text.
Hugging Face explains how Inference Endpoints, Jobs, and Buckets power semantic search on Papers with Code.
Hugging Face describes the infrastructure behind search on Papers with Code, built on its Inference Endpoints, Jobs, and Buckets services. The post is a product-focused engineering walkthrough with no security impact.
M-A-P released YuE2-3B, an open music generation model that outperforms Suno v5 on WildSongBench and runs locally on a 24GB GPU.
The M-A-P (multimodal-art-projection) team released YuE2-3B, an open-weights music generation model that turns lyrics and a style prompt into full songs with vocals and accompaniment. It uses an AR-NAR Mixture-of-Transformers backbone with symbolic planning and flow matching through a VAE, and supports editable scores (melody and chords, including ABC notation) plus agentic editing workflows. On 192 WildSongBench prompts it reports a SongBench average of 6.9632 (best-of-8) versus 6.8721 for Suno v5, claimed as state of the art among evaluated open and proprietary models. It runs 48 kHz stereo inference locally on a single 24GB NVIDIA GPU without quantization, with companion releases including YuE2-Vae, MERT-v2 encoders, the WildSongBench dataset, and SheetSage2.
Training-free reasoning-plus-retrieval framework lifts multilingual multimodal entity linking accuracy by 6.9% overall and up to 23.3% on rare entities.
The paper broadens rarity measurement in multimodal entity linking using knowledge-graph structural metrics beyond popularity-based pageview metrics, identifying many rare entities that popularity metrics miss. Across these rare-entity slices, state-of-the-art accuracy drops by 15.4-39.9%, exposing distinct failure modes under different rarity definitions. The proposed training-free framework iteratively searches and reasons over Wikipedia with a reasoning-capable vision-language model; experiments show reasoning and retrieval are complementary. On the MERLIN benchmark covering Hindi, Indonesian, Japanese, Tamil, and Vietnamese, the system improves overall accuracy by 6.9% and up to 23.3% on rare entities, with MERLIN-Rare test slices released.
Next.js 16.4.0-canary.13 Image Optimizer retains a DNS rebinding TOCTOU SSRF in fetchExternalImage() allowing requests to private network resources.
Next.js 16.4.0-canary.13 contains a DNS rebinding time-of-check-to-time-of-use Server-Side Request Forgery vulnerability in the Image Optimizer's fetchExternalImage() function. The code resolves the supplied hostname and checks the resulting addresses with isPrivateIp(), but a TOCTOU gap between resolution and the actual fetch allows an attacker-controlled DNS record to change and reach private network resources.
Researchers decompose normalizing flows to derive near-pivotal statistics for likelihood-free inference with nuisance parameters, recovering the t-test and beating Welch limits.
A new paper decomposes neural-network normalizing flows to uncover pivotal statistics in the presence of nuisance parameters using only a sample generator from the distribution of interest. The statistic is near-pivotal in the sense of minimum average KL-divergence of its p-values and can incorporate prior knowledge of group invariances such as translation and scale. Experiments show it recovers the one-sample t-test almost exactly, outperforms the Welch test on worst-case size over a constrained variance-ratio range, and delivers higher power and much faster runtime than profile likelihood-ratio techniques on small-to-moderate samples.
QPriv-VL prunes privacy-sensitive visual tokens in federated/split VQA, cutting membership-inference success on VQA-RAD from 0.99 to 0.76-0.79 using ~40% of tokens.
The paper proposes QPriv-VL, a question-guided token-pruning framework for federated, split, and U-shaped split learning that suppresses privacy-sensitive visual patches before transmission. Its Dynamic Threshold Predictor combines cross-modal question relevance with frozen DINOv2-derived sensitivity to compute a per-sample pruning ratio and retention mask in one forward pass, without sensitivity labels. Evaluated on GQA, OK-VQA, VQAv2, SLAKE, VQA-RAD, and PathVQA against FSHA, FORA, iDLG, and attribute-inference membership inference attacks, it matches or beats fixed-ratio pruning. On VQA-RAD it reduces membership-inference success from 0.99 to 0.76-0.79 while preserving competitive accuracy with about 40% of the original token budget.
DeepSeek releases DeepSeek-V4.1-Flash, a 552B-parameter multimodal MoE model with 1M-token context and KV cache cut to 890 bytes per token.
DeepSeek-V4.1-Flash is a multimodal Mixture-of-Experts model with a 552B-parameter backbone that activates 8B parameters per token during prefill and 16B during decode. It uses a Causal Encoder-Decoder architecture, Compressed Sparse Attention 2, and FP4 KV caching to reduce the global KV cache footprint to 890 bytes per token, roughly one quarter of DeepSeek-V4-Flash. The model was trained from scratch on 45T tokens with context extended to 1M tokens, includes an Engram conditional-memory module (196B parameters), and is released under the MIT license. Post-training uses SFT, RL, and on-policy distillation with large-scale automated synthesis of agentic tasks and a controllable reasoning effort setting from 1 to 100.
m-a-p's YuE2-3B music generation model and SheetSage2 audio encoder are repackaged in bf16 for ComfyUI and trending #30 on Hugging Face.
Comfy-Org published repackaged bf16 safetensors files for m-a-p's YuE2-3B model and its SheetSage2 audio encoder, organized into ComfyUI checkpoints and audio encoder folders. The repository links to the original m-a-p/YuE2-3B and m-a-p/SheetSage2 model pages and is currently trending #30 on Hugging Face.
RelateAnything is a 53M-parameter open-vocabulary relation prediction model running at 20 ms/frame, with 2.3-3.5x higher mean recall than comparable open-vocabulary methods.
RelateAnything predicts scored relations between image regions using any predicate vocabulary supplied at inference as text embeddings, with object labels never required as input, so region sources can change without retraining. Training covers 19,103 predicates using positive-unlabeled supervision; the authors release RA-4M (474k images, 4.3M geometrically verified relations over 10,102 free-text predicates) and the OV-SGG-Bench evaluation suite. The 53M-parameter model runs at 20 ms/frame and achieves 2.3-3.5x the mean recall of the strongest comparable open-vocabulary method across cross-dataset and zero-shot benchmarks. Model, corpus, and benchmark are public.
AIJON uses LLMs to auto-generate IJON-style fuzzing annotations, matching human-expert quality on the Magma benchmark.
Researchers replicate IJON experiments and propose AIJON, a system that uses LLMs to automatically generate expert-style annotations for coverage-guided fuzzing at scale. On the Magma benchmark, LLM-generated annotations performed comparably to human-generated ones, though annotation-based fuzzing was not strictly better than AFL++. The paper analyzes how annotations affect fuzzer energy distribution across campaigns.
SAS trains attention sparsification end-to-end with the language modeling loss, beating sparse attention baselines especially under tight context budgets.
Simple Attention Sparsification (SAS) injects the selector's continuous scores into attention logits in log form inside the softmax, letting gradients from the language modeling loss directly update the ranking of context units. The method uses normalized softmax gates calibrated against the current block and a memory-efficient Triton kernel integrated into FlashAttention-style computation. Across reasoning, long-context, and agentic tasks, SAS consistently outperforms trainable sparse attention baselines across budgets, with the largest gains under tight attention budgets.
CoVeR, a training-free coverage-based token pruner, preserves 93.5% of VLM 3D-reasoning performance using only about 8% of visual tokens.
Researchers introduce CoVeR, a deterministic, training-free selector that chooses visual tokens to cover every region of a multi-view 3D scene using only token coordinates. Unlike learned-importance and voxelization pruners, it enforces an exact per-scene token budget, avoids saturation plateaus, and prevents near-duplicate selections. Experiments across four vision-language models show it surpasses prior state of the art by 3.9 percentage points on average across three 3D reasoning benchmarks.
PARSER uses parallel reader subagents and an RL-trained lead agent for long-context QA, beating baselines and cutting latency up to 11x.
The PARSER paper decouples reading from reasoning: frozen subagents each read one document chunk in parallel while an RL-optimized lead agent iteratively broadcasts queries and aggregates evidence in scatter-gather rounds. On multi-hop QA with 7K to 896K token contexts, a 4B-backbone PARSER beats the strongest sequential memory baseline by 5.7 points on average and 12.0 points at 896K tokens, and a 9B version surpasses DeepSeek-V4-Pro by 6.3 points. Controlled experiments show robustness to evidence position, order, and distance perturbations, with inference latency reduced by up to 11x.
The 2026 PNPL competition releases LibriBrain100, a MEG speech dataset with 32 extra subjects, targeting word classification and cross-subject BCI generalization.
The 2025 PNPL competition on non-invasive speech decoding from MEG achieved F1-macro scores of 95.6% for speech detection and 73.6% for phoneme classification, built on LibriBrain's ~50 hours of single-subject data. The 2026 edition extends this with LibriBrain100, adding 32 subjects (~40 minutes each) plus ~80 hours of within-subject data. Two tracks target within-subject word classification at scale and cross-subject generalization with subject-specific fine-tuning shrinking from ~40 to ~20 to ~10 minutes, aiming at clinically feasible non-invasive BCIs for people with profound paralysis.
Researchers propose a claim-level discourse framework for health videos, finding 13.22 atomic claims per minute and that LLMs struggle with pragmatic profiling.
The paper introduces a structured framework for claim-level discourse analysis in dense health narratives on social media videos, modeling tuples that link atomic claims with thematic aspects, stance, and multidimensional pragmatic attributes. Analysis found an average of 13.22 atomic claims per minute in health video discourse. A benchmark spanning four health domains with 1,191 manually annotated claims from 60 videos shows current LLMs perform strongly on thematic categorization and stance prediction but struggle with high-dimensional pragmatic profiling, suggesting future systems need task decomposition and specialized inference strategies.
ISTA-DASLab releases GSQ-RCO non-uniform GGUF quantizations of Qwen3.8-27B down to 2.5 bpw, with task-lossless IQ3_S matching BF16 benchmark scores.
ISTA-DASLab released GGUF quantizations of Qwen3.8-27B produced with GSQ (Gumbel-Softmax Quantization) and RCO (Riemannian Constrained Optimization), non-uniform methods that allocate per-tensor precision via gradient-based search under a total size budget. Four checkpoints range from 2.50 bpw (8.4 GB) to 3.50 bpw (11.8 GB), plus a BF16 vision projector (mmproj) enabling multimodal use. The recommended IQ3_S build is task-lossless, matching the BF16 base exactly on AIME25 (100.00) and LiveCodeBench v6 (85.71) at roughly one fifth of the BF16 size. Optional -mtp variants add a Multi-Token Prediction head for speculative decoding in llama.cpp.