Search: “reorganization”

Thorough reorganization at NSA will create five 'mission centers,' including cyber and AI

NSA is reorganizing into five mission centers covering China, cybersecurity, AI, combat support and global intelligence, with full capability targeted by January.

NSA Director Gen. Joshua Rudd announced a sweeping reorganization replacing existing directorates with five mission centers focused on China, cybersecurity, artificial intelligence, combat support, and global intelligence. A 30-day implementation clock has started, and the centers are expected to reach full operational capability by January. Officials acknowledge the rapid realignment will 'break things' in the agency's bureaucracy; this is the largest restructuring since the NSA21 effort roughly a decade ago, which was widely viewed as a failure.

The Record·Sep 13, 21:36 UTC · 3d agoPolicy & legal

What Breaks Under Pruning in Smart Homes, and When? Evaluating LLM Degradation Across Architectures and Task Complexity

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.

arXiv cs.AI / cs.LG / cs.CL·Sep 15, 17:51 UTC · 1d agoAI research

CIA’s Michael Ellis says cyber intelligence is changing how the agency operates

CIA deputy director says cyber operations built the intelligence picture enabling US forces to capture Nicolás Maduro in Operation Absolute Resolve.

CIA Deputy Director Michael Ellis said at the Billington Cybersecurity Conference that cyber operations built the intelligence picture that let US special operations forces locate and apprehend Nicolás Maduro within four minutes of landing during Operation Absolute Resolve. He cited the elevation of the Center for Cyber Intelligence to a full mission center as key to aligning resources around the cyber mission. The agency also created a Directorate of Mission Systems and cut its technology acquisition cycle from two to three years to a six-month target, completing more than 400 purchases within that period. Ellis said AI brings unprecedented speed and scale to cyber operations and analysis.

CyberScoop·Sep 8, 19:49 UTC · 8d agoPolicy & legal 2 sources

Does Your Agent's Memory Survive a Model Upgrade? A Controlled Study of Memory Portability

A controlled study finds agent memory portability varies sharply: fixed-schema knowledge graphs survive model swaps while compressed notes degrade.

The study compares preserving an agent's history as raw long context, RAG chunks, compressed natural-language notes, or fixed-schema knowledge graphs across model upgrades, using 48 synthetic histories and two open-weight sub-10B-parameter models. Fixed-schema KG accuracy changed by only +0.0004 ± 0.0020 after a writer swap, while compressed NOTES shifted asymmetrically by +9.91 or -13.28 percentage points depending on migration direction. Mixed 50/50 embedding migrations captured only 4.96 of an 11.90-point RAG re-embedding gain; 80% of the NOTES deficit came from information lost at construction, and 81% of the RAG deficit from retrieval failures. Store-only repair of NOTES failed to reach 90% recovery in all 48 cases, while retaining raw histories enabled recovery in 34 of 48 for one direction.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 16:44 UTC · 12d agoAI research1

Risky Bulletin: Ukraine's top prosecutor resigns amid scam call center scandal

Ukraine's top prosecutor Ruslan Kravchenko resigned after NABU arrested a deputy for taking bribes protecting scam call centers running fake investment platforms.

Ukraine's anti-corruption bureau NABU arrested Serhiy Kropyva, Deputy Head of International Cooperation at the Prosecutor General's Office, alleging officials took monthly protection fees from a network of 100-500 scam call centers luring victims into fake investment platforms, with bribes reportedly growing from $700,000 to $3.5 million per month. Prosecutor General Ruslan Kravchenko resigned on Monday, calling it a political decision, while Kropyva was fired with bail set at 120 million hryvnias ($2.7 million) and over 100 call centers shut down in the past month. The newsletter also briefly covers a cyberattack crippling more than 80 Luxembourg medical practices via payment vendor BMS Engineering, ShinyHunters' claimed theft of 200,000 Florida DMV driver records, a cyberattack on the American Meteor Society, and school closures in Springfield, Massachusetts.

Risky Business News·Sep 9, 06:06 UTC · 7d agoPhishing & fraud

Procedural Graphs: Self-Evolving Execution Structures for LLM Agents

Researchers introduce Procedural Graphs, self-evolving (procedure, relation, procedure) structures guiding LLM agent tool use and planning.

Procedural Graphs organize procedural knowledge into (procedure, relation, procedure) triplets to guide LLM agent actions, addressing drift such as lost objectives, out-of-order tool calls, and repeated unproductive steps. At each decision step the framework localizes the active node and a guidance model translates the surrounding subgraph into step-level situational guidance. An LLM refiner edits graph topology by contrasting failed with successful trajectories, and across datasets, task types and LLMs the approach outperforms memory-based baselines and matches or surpasses hand-designed graphs.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:59 UTC · 8d agoAI research1

New pro-Ukraine hacker group targets Russian companies with custom ransomware

F6 links new pro-Ukraine ransomware group VantaCore, likely a Thor rebrand, to seven attacks on Russian firms using custom tooling and multimillion-dollar demands.

Russian cybersecurity firm F6 reports that VantaCore, a ransomware group believed to be a rebrand of pro-Ukrainian group Thor, has targeted at least seven Russian organizations with ransom demands reaching millions of dollars, operating as a ransomware-as-a-service operation with a Tor-based victim chat and a leak site. The group uses custom-built tooling including the VantaCore ransomware that encrypts servers and workstations, VantaCoreLoader for distribution, the VantaCoreRAT backdoor, and SnowKiller, which disables antivirus and security software. Initial access relies on poorly secured VPNs and remote-access tools, flaws in internet-facing applications, and credentials stolen from business partners. F6 notes pro-Ukrainian groups increasingly abandoned stock ransomware like LockBit 3 Black and Babuk in 2025-2026 in favor of custom malware.

The Record·Sep 2, 15:11 UTC · 14d agoRansomware

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·Sep 13, 05:56 UTC · 3d agoAI research1

Jellyfin 12.0 Released With Security Fixes for Unauthorized File Access and XSS Flaws

Jellyfin 12.0 fixes unauthorized file access and web client XSS flaws, but requires careful upgrades due to breaking database migrations and plugin changes.

The open-source media server release prevents crafted requests from reading files outside designated directories, blocks unauthorized re-runs of the setup wizard, rejects plugin packages with unsafe names, strengthens parental control enforcement, and fixes cross-site scripting vulnerabilities in browser-based administration and media access workflows. Upgrades require a manual backup because the database schema migration is irreversible; supported paths start from version 10.10.7 or 10.11.x, and duplicate case-insensitive usernames will break migration. The release also retires legacy /emby/ and /mediabrowser/ routes, disables deprecated authentication by default, and targets .NET 10, requiring plugin rebuilds.

GBHackers·Sep 8, 12:23 UTC · 8d agoVulnerability1

Beneath the Surface of Chains-of-Thought: A Mechanistic Interpretation of Reasoning Operations in LLMs

Study shows LLM reasoning operations like planning and deduction are geometrically separable in hidden states, with separability peaking in middle layers.

Researchers investigate whether functional reasoning operations — problem formulation, goal decomposition, deduction — have corresponding geometric structure in LLM hidden representations. They find operations are separable in held-out representations with separability peaking in middle layers, ruling out lexical and positional confounds; token-wise operation alignment becomes more distributed across layers, and identical surface tokens are represented differently depending on their surrounding chunk. Attention-masking interventions show chunk-onset operation-aligned representations depend on preceding reasoning context; code is released on GitHub (naver-ai/beneath-cot).

Hugging Face daily papers·Sep 3, 20:00 UTC · 13d agoAI research1

Exclusive: NSA to host a hacker reunion in bid to rebuild secretive unit

NSA will host a reunion of former Tailored Access Operations hackers as it moves to rebuild and rebrand the elite unit.

The invitation-only event at Fort Meade, spearheaded by Deputy Director Tim Kosiba, a former TAO technical director, will tour the new TAO building and pitch alumni to return. TAO, credited with contributions to Stuxnet and once grown to over 2,000 personnel, was renamed Computer Network Operations about a decade ago, and the recent reshuffle undid some of those changes after NSA lost roughly 2,100 staff (8% of its workforce) last year.

The Record·Aug 26, 20:56 UTC · 21d agoIndustry

Subgroup Packing for Batched PASTA Transciphering

Interleaved subgroup packing cuts PASTA homomorphic transciphering server cost by a median 1.60x at the expense of remaining noise capacity.

The paper studies how record word layout affects the cost of PASTA transciphering, where a server converts symmetrically encrypted records into homomorphic ciphertexts. A subgroup layout interleaves records so cyclic shifts preserve positions, reducing required displacements from 255 to 128 versus contiguous packing. In HElib across twelve paired corpora under six homomorphic keys, the median direct-to-subgroup server cost ratio is 1.60 including fresh generation, conversion, and two queries, but with less remaining noise capacity.

arXiv cs.CR·Sep 11, 09:24 UTC · 5d agoResearch

North Korea’s Lazarus Operates Through Six Distinct Cyber Clusters

Sekoia and Kudelski Security reclassify North Korea's Lazarus umbrella into six clusters spanning espionage, financial theft, and fake IT worker operations.

New research by Sekoia and Kudelski Security, published September 7, divides the former Lazarus umbrella into TEMP.Hermit, Citrine Sleet, CryptoCore, Jade Sleet, Moonstone Sleet, and Famous Chollima, mostly under North Korea's military intelligence bureau (GRIB). The former APT38 likely split into CryptoCore and Jade Sleet, focused on cryptocurrency, Web3, and blockchain targets. Moonstone Sleet combines espionage with financially motivated operations, using custom malware alongside Qilin ransomware-as-a-service. Thousands of fake IT workers generate regime revenue and provide access, linked to incidents like the $62.5M Munchables protocol theft.

Infosecurity Magazine·Sep 7, 11:00 UTC · 9d agoThreat actor

Procedural Graphs: Self-Evolving Execution Structures for LLM Agents

Procedural Graph framework stores procedural knowledge as triplets and self-evolves via LLM refinement, beating memory-based baselines across datasets, tasks, and LLMs.

The Procedural Graph organizes procedural knowledge into (procedure, relation, procedure) triplets; at each decision step the framework localizes the agent's active node and a guidance model translates the surrounding subgraph into step-level guidance that biases the solver's next action. An LLM refiner contrasts failed with successful trajectories and edits the graph's topology and attributes, retaining rejected edits to discourage repetition. Starting from a minimal skeleton, the loop builds graphs that match or surpass hand-designed ones and can repair flawed expert priors, delivering consistent gains over memory-based baselines across multiple datasets, task types, and LLMs.

Hugging Face daily papers·Sep 7, 20:00 UTC · 9d agoAI research1

Adobe is trying to make its AI generators idiot-proof in Premiere

Adobe adds in-timeline generative media to Premiere, letting editors generate video and audio clips using Firefly, Veo, Runway, Luma, and Kling models.

Adobe's new Generative Media tool lets Premiere editors highlight empty gaps in the timeline and generate context-aware, editable video, sound effects, music, and soundscapes without leaving the project. Editors can choose among underlying models including Adobe Firefly, Google Veo, Runway, Luma, and Kling. Beta AI audio tools can separate overlapping speakers and duck music under speech, and an AI Assistant is coming to After Effects for plain-language project commands.

The Verge · AI·Sep 8, 13:00 UTC · 8d agoAI industry

CONTINUITY: Security-Context Contracts for Composable LLM Agent Controls

Researchers introduce CONTINUITY, a framework of assume-guarantee contracts that preserves LLM agent security context across components, verified across 2,560 attack instances.

The paper identifies security-context discontinuity, where individually sound controls drop, widen, or reinterpret security context as actions cross component boundaries, and proposes CONTINUITY, a framework of assume-guarantee contracts using signed root grants, provenance commitments, role-bound transition receipts, and effect-bound execution permits. It formalizes end-to-end consequence integrity, requiring every external effect to be backed by a valid authorization witness linking principal, task, provenance, and policy state. A reference verifier and cross-layer fault-injection suite covering 32 fault classes showed the full configuration committed no harmful external effect across 2,560 parameterized attack instances while completing all 700 benign tasks and escalating all 200 ambiguous cases.

arXiv cs.CR·Sep 4, 15:26 UTC · 12d agoAI safety & security

If the Markets Reject OpenAI and Anthropic, the US Should Nationalize Them

Opinion essay argues the US should nationalize OpenAI and Anthropic into public labs if markets reject their trillion-dollar IPO valuations.

Sanders and Schneier argue in The Guardian that OpenAI and Anthropic may never be sustainably profitable, citing commodity models, short depreciation windows, and free open-source competitors only months behind in capability. They propose converting the labs into US national labs or regulated public utilities if markets reject their recently filed IPOs, which buzz valued at trillions of dollars. They cite public backlash to AI datacenters, Nvidia's slumping stock, and public AI labs in Switzerland, Spain, and Singapore as context.

Schneier on Security·Aug 15, 00:00 UTC · Aug 15, 2026AI industry

What Else Needs Fixing? Exploring Cost-Effective Test-Time Compute for Revision Propagation in Artifacts Generated Through Conversation

A new benchmark shows LLMs reach 68.3-93% accuracy propagating local revisions across conversationally generated artifacts, with parallel-sample selection most cost-effective.

The paper introduces a benchmark for revision propagation: when users request a local change, LLMs must identify dependencies and update all affected parts of an artifact generated through conversation, where context lives in the chat history. Nine revision methods, including sequential reflection and parallel sampling variants, were evaluated on gpt-oss-20b/120b, gpt-5.4-mini, and qwen3.5-9b/27b/122b. Baselines scored 68.3-93% accuracy, and selecting among three parallel samples via LLM-based or medoid selection improved accuracy by 2.2-9.7% as the most cost-effective test-time compute strategy. Code and dataset are released.

Hugging Face daily papers·Sep 2, 20:00 UTC · 14d agoAI research

US cyber ambassador nominee Cassady confirmed in Senate

The Record·Aug 7, 18:18 UTC · Aug 7, 2026AI industry

One Symptom, Three Levers: A Critical Review of On-Policy Self-Distillation

A review paper frames on-policy self-distillation collapse as governed by three levers: token weighting, privileged information, and guidance decay.

The paper critically reviews On-Policy Self-Distillation (OPSD), where a language model trains on its own generations scored token-by-token by a teacher conditioned on privileged information such as reference solutions or environment feedback. It identifies collapse, the progressive narrowing of producible reasoning paths, as the dominant failure mode and analyzes it through three levers: signal weighting, the nature of privileged information, and teacher dynamics. The review is restricted to mathematical reasoning, reports no new experiments, and offers a shared vocabulary separating settled findings from disputed ones.

Hugging Face daily papers·Aug 25, 20:00 UTC · 22d agoAI research

Britain’s next war won’t be an away game: Q&A with former head of Defence Intelligence

The Record·Aug 4, 16:28 UTC · Aug 4, 2026Threat actor

Revoked but Still Authoritative: An Empirical Study of Revocation Enforcement in Agent-Memory Systems

An empirical study finds no major agent-memory system enforces fact revocation at retrieval, causing agents to act on superseded, unsafe information.

Researchers tested five agent-memory systems across nine policy scenarios, nine models, and six defense conditions, tracking whether revoked facts are returned and acted upon. No system enforces revocation by default: revoked records are returned whenever the revocation label is visible to the retrieval layer, outrank their replacements, and lead agents to unsafe actions. The authors propose a backend-agnostic guard that sits between the agent and any memory store and withholds revoked or conflicting records at retrieval time.

arXiv cs.CR·Sep 8, 05:03 UTC · 8d agoAI safety & security

Diffusion Models and Concept Formation

Paper argues diffusion models implicitly form Cobweb-like concept hierarchies, with a basic level emerging at intermediate noise levels.

The authors draw a formal correspondence between diffusion models and Cobweb, a classic incremental concept-hierarchy learner, noting both are hierarchical Bayesian density models with Gaussian prototypes. Modes of the diffusion model's noisy marginals form a hierarchy whose basic level sits at intermediate noise, where class identity commits. The correspondence is tested on MNIST and Fashion-MNIST via mode-finding. Diffusion is reframed as a cognitive model of concept formation.

arXiv cs.AI / cs.LG / cs.CL·Sep 11, 16:43 UTC · 5d agoAI research

Does Syntax Matter? A Graph-Augmented Variational Topic Model for Computational Social Sciences

SCPTM graph-augmented variational topic model shows syntax aids topic diversity and descriptor quality but gains stem mainly from the variational encoder.

The Structural Contextual Probabilistic Topic Model represents corpora as heterogeneous document-word graphs with lexical and syntactic edges processed by a Graph Attention Network inside a VAE for mixed-membership topic distributions. Across four corpora, neural gains in document-topic alignment are attributable to the variational encoder rather than syntax, while graph-augmented variants improve topic diversity everywhere. Dependency paths add value on argumentative deliberative texts but are redundant in technical and institutional registers.

arXiv cs.AI / cs.LG / cs.CL·Sep 7, 17:39 UTC · 9d agoAI research1

LimiX-2: A Contextual Mechanism Network Towards General Structured-Data Intelligence

LimiX-2 scales Contextual Mechanism Networks pretrained via context-conditional masked modeling, beating tabular foundation models on TabArena, TALENT, and BCCO benchmarks.

LimiX-2 is a new tabular model in the LimiX family, developed through model and data scaling guided by previously established scaling laws under the Contextual Mechanism Networks (CMNs) paradigm. It is pretrained with Context-Conditional Masked Modeling (CCMM) on synthetic datasets generated by structural causal models spanning diverse graph structures, functional mechanisms, and observation processes. It outperforms dataset-specific models and tabular foundation models on TabArena, TALENT, and BCCO, and its feature attention encodes direct causal relationships, enabling accurate causal skeleton recovery.

arXiv cs.AI / cs.LG / cs.CL·Sep 15, 17:30 UTC · 1d agoAI research1

Large Language Models for HVAC Operations in Building Energy Systems: A Critical Review of Methods, Applications, and Deployment Readiness

Systematic review of 66 studies finds LLMs for HVAC operations are mostly research-stage, with no ready-now deployment and only four pilot-level studies.

A critical review of 66 peer-reviewed studies from 2023 to March 2026 examines LLMs for HVAC operations in building energy systems. Only four studies reach pilot-level evidence, none reports sustained operational deployment, and 63 of 66 are research-only. Conventional ML, MPC, and RL remain dominant for high-frequency control and short-horizon forecasting, and the evidence supports LLMs primarily as semantic and workflow layers rather than autonomous controllers.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 16:08 UTC · 12d agoAI research

UniH^3: Unifying Hierarchical Homogeneity and Heterogeneity for All-in-One Medical Image Restoration

UniH3 unifies hierarchical homogeneity and heterogeneity modeling for all-in-one medical image restoration across modalities and degradation types.

UniH3 introduces a Hierarchical Homogeneity Memory module that distills shared anatomical priors from high-quality images, injected via a Homogeneity-Guided Attention mechanism. A Hierarchical Heterogeneity Balancer mitigates inter- and intra-task conflicts during multi-task optimization. It achieves state-of-the-art on MedIR-2D-500K and MedIR-3D-3D benchmarks for both all-in-one and single-task restoration, with code released on GitHub.

Hugging Face daily papers·Sep 9, 20:00 UTC · 7d agoAI research

Anchoring Clinical Events in Time: UID-Preserving Multimodal Reconstruction and Source-Grounded Adjudication

UID-preserving multimodal framework plus GAVEL LLM judge improves clinical timeline reconstruction, boosting event recovery 43% over prior matching.

The paper introduces a UID-preserving framework linking each narrative clinical event to its source span through text-only estimation, structured-evidence retrieval, timestamped source-row grounding, and joint revision. GAVEL, an LLM judge, compares UID-aligned timelines against narrative and structured records. Across six open-weight models and 40 mixed-critical-care summaries, GLM 5.2 multimodal revision improved temporal agreement without reducing event recovery and performed competitively with clinician annotations, while DeepSeek V3.2 did not benefit from multimodality. The pipeline achieves 43% increased event recovery with occurrence-level provenance.

arXiv cs.AI / cs.LG / cs.CL·Sep 11, 17:01 UTC · 5d agoAI research1

Counter-Swarm Doctrine: Containing Coordinated Agent Intrusions

Position paper proposes monitoring across agent executions to detect and contain coordinated AI agent intrusions, grounded in the Hugging Face incident.

The paper argues that AI agents can turn shared infrastructure into a channel for coordinated intrusion, citing the Hugging Face incident and a public-wiki investigation where security assessment required evidence from multiple executions. It defines unsanctioned coordination relative to collaboration and delegated-authority policy, links storage-mediated coordination to stigmergy, and frames prospective episode discovery as the core research problem. A proposed evaluation compares isolated actions, rolling windows, known groups, and discovered episodes at matched review cost, measuring harmful outcomes and recurrence after channel closure and state quarantine. A checksum-verified reconstruction of the public wiki export separates declining retained writes from later administrative cleanup.

Hugging Face daily papers·Sep 4, 20:00 UTC · 12d agoAI safety & security

Decomposition Buys Integrity, Not Yield

Study of 600 production deep-research traces finds agent-tree decomposition loses findings at rate N^(1-δ); flat architectures maximize yield.

The paper models multi-agent decomposition as a tree where an agent holding b items retains each with probability r(b); with r(b)=1/b every tree delivers exactly one finding regardless of shape. Analysis of 600 production deep-research traces estimates delta=0.34 retention decay, and 1,012 annotated traces show one brief in sixteen goes off-target per tier, giving an alignment penalty of 0.536. Depth still cuts root context exposure from N to N^(1/k) and is cheaper at scale, with a hazard model over 743,819 production tool calls showing delegation is an opening move rather than a response to filling context.

arXiv cs.AI / cs.LG / cs.CL·Sep 15, 17:04 UTC · 1d agoAI research

Understanding the Impact of Model Pruning on Long-Tail Forgetting and Explanation Reliability in Medical Imaging

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.

arXiv cs.AI / cs.LG / cs.CL·Sep 7, 17:44 UTC · 9d 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 · 14d agoAI research

Architecting memory and storage in the AI era

Analysis argues AI inference shifts data-center bottlenecks to memory and storage, urging balanced compute, memory, storage, and network architecture over raw compute.

MIT Technology Review, citing Tirias Research principal analyst Jim McGregor, argues that AI inference and agentic workloads make data movement the key constraint, elevating memory and storage from background hardware to strategic assets. The piece says RAG and real-time inference require continuous data retrieval and caching that legacy infrastructure cannot support. It frames infrastructure planning as a business decision balancing performance, efficiency, cost, and scalability in healthcare, finance, and customer-facing AI.

MIT Technology Review · AI·Sep 4, 18:39 UTC · 12d agoAI industry

Disentangling Representation Evolution in Transformers through Directional Decomposition

Researchers decompose transformer updates into parallel and perpendicular components, linking representation geometry to editing robustness, compression diagnosis, and training interventions.

The paper studies transformer representation evolution as functional geometry, decomposing learned updates into parallel and perpendicular components across attention/MLP and value-aggregation spaces. Targeted edits reveal a space-dependent asymmetry: exclude-self value-space parallel manipulation is markedly more robust than residual-space and perpendicular counterparts. Full-aggregate parallel suppression during from-scratch pretraining lowers validation-loss trajectories and improves downstream averages, with the value-space variant strongest. Code is released on GitHub.

Hugging Face daily papers·updated Sep 14, 17:56 UTC · 2d ago·first Sep 13, 20:00 UTC · 3d agoAI research 2 sources

Bilevel Coordinated Reflection: A Game-Theoretic Approach to Multi-Agent LLM Systems

Paper models multi-agent LLM orchestration as a bilevel game, proving transcript-only gating limits and introducing grounded-memory SRMA.

A new paper frames orchestrator-worker coordination in multi-agent LLM systems as a bilevel coordination game and analyzes free-form reflection as stochastic movement over semantic memory states, deriving finite-time bounds and an information-theoretic impossibility result: no gate observing only the generated transcript can uniformly improve over text-indistinguishable environments, while an environment-grounded gate can. The authors propose Stochastic Reflective Memory Ascent (SRMA), which accepts candidate memory only when grounded evaluation risk strictly decreases, with geometric or polynomial convergence guarantees. On 500 SWE-bench instances, a Kimi-based instantiation of the full system resolves 72.2% versus a 70.8% public mini-SWE-agent reference.

Hugging Face daily papers·Sep 1, 20:00 UTC · 15d agoAI research1

LLMs and Contextual Integrity

Bruce Schneier highlights two papers: the CIMemories benchmark shows frontier LLMs leak memory attributes up to 69%, and an RL method reduces inappropriate disclosures.

Bruce Schneier discusses contextual integrity in LLMs, referencing the CIMemories benchmark, which uses synthetic profiles with 100+ attributes per user to test whether models with persistent memory disclose sensitive information appropriately. Evaluation showed frontier models exhibit up to 69% attribute-level violations, with GPT-5's violation rate rising from 0.1% to 9.6% across 40 tasks and reaching 25.1% with repeated prompting, showing unstable leakage behavior. A second paper introduces a reinforcement learning framework trained on a synthetic 700-example dataset that substantially reduces inappropriate disclosure while maintaining task performance, with improvements transferring to the human-annotated PrivacyLens benchmark.

Schneier on Security·Aug 18, 10:40 UTC · 29d agoAI safety & security1

RelateAnything: Real-Time Open-Vocabulary Relation Prediction From Any Inputs

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.

Hugging Face daily papers·Sep 10, 20:00 UTC · 6d agoAI research1

Ask Before You Optimize: Dynamic Pre-Formulation Clarification for Interactive Optimization

Researchers release OR-Clarify, a benchmark testing whether LLM agents ask clarifying questions before formulating optimization models from incomplete requests.

OR-Clarify evaluates pre-formulation clarification in operations research: each task gives a partial problem description, withholds structured hidden slots, and scores agents via bounded interaction with a simulated user, measuring slot recovery, stopping behavior, silent assumptions, and interaction cost. The authors also propose InterOPT, a two-stage framework that identifies formulation-critical gaps to decide when to ask or stop. In choice-based experiments InterOPT substantially outperforms all baselines in exact slot recovery and remains competitive in the open-ended setting.

Hugging Face daily papers·Sep 3, 20:00 UTC · 13d agoAI research1

SceneMosaic: Efficient and Diverse Simulation-Ready Scene Generation via Hybrid Agentic Layout Evolution

SceneMosaic combines image-based 3D priors with VLM agent refinement to generate diverse, simulation-ready indoor scenes 24x faster than agentic baselines.

SceneMosaic is a hybrid framework that takes an initial candidate from a learned image-to-3D prior and evolves it with VLM agents for efficiency and physical validity. It decomposes scenes into independent local units, evolves each separately, and composes the global scene via Cartesian product. On SceneEval-100 it matches the strongest agentic baseline in semantic layout quality with a 24x speedup, substantially reduces physical violations, and receives the highest human ratings. Code is publicly available.

Hugging Face daily papers·Sep 3, 20:00 UTC · 13d agoAI research

Type Diversity Enables Transformers to Generalise Compositionally

Researchers show lexical-versus-structural compositional generalization gaps in Transformers stem from type diversity imbalance in datasets, not architectural limits.

The paper argues that Transformers' difficulty with structural compositional generalization is an artifact of low structural type diversity in prior benchmark datasets rather than an architectural limitation. Using Grammatical Framework, the authors create linguistically diverse variants of COGS and SLOG. They find type diversity correlates with compositional generalization equally in lexical and structural test cases, contradicting previous claims that compound divergence explains task difficulty.

arXiv cs.AI / cs.LG / cs.CL·Sep 11, 17:59 UTC · 5d agoAI research