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How much of F-Droid is LLM generated?
A FOSS maintainer manually graded 102 F-Droid apps from the September 12, 2026 update batch, finding many show signs of LLM-generated code.
A student and FOSS app maintainer reviewed 102 apps pushed to F-Droid on September 12, 2026, assigning each a three-tier rating for likelihood of LLM-authored code (mostly AI >50%, hard to say/mostly human, no signs of AI). The heuristic relies on commit aesthetics, README and branding style, and the presence of agentic infrastructure like Claude Code or Codex, which automatically places an app in the 'mostly AI' tier. Example ratings include Amber (Nostr event signer) as mostly AI, and Aria for Misskey as showing no AI signs. The author stresses reliable detection of LLM-generated code from text alone is impossible, so ratings are approximate.
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Most of the bugs Claude Mythos found have never been checked by a human
Echo's analysis found only 1,900 of 23,019 Claude Mythos-found vulnerabilities were externally reviewed, 90.8% held up, but the model overstated most severities.
Echo analyzed results from Anthropic's Claude Mythos Preview vulnerability sweep across 281 open-source projects, which produced 23,019 candidate vulnerabilities, of which only 1,900 were externally reviewed. Of those, 90.8% held up as real, 1,451 of 1,596 maintainer reports were acknowledged, 97 fixes landed upstream, and 88 became advisories, but 14 of the 27 CVE-assigned severity ratings mismatched independent scoring, mostly overstated. On Anthropic's SpiderMonkey benchmark, Claude Mythos turned known crashes into working code execution exploits in 72.4% of 250 trials, versus below 1% for Claude Opus 4.6. Echo cautions the reviewed sample likely was not randomly drawn, so the accuracy figure may not generalize to the other 21,119 unreviewed candidates.
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Agent Harness vs Agent Framework vs MCP: Which Layer Owns the Loop, State, Tools, Permissions, and Recovery
Architecture explainer separates agent harnesses, frameworks, and MCP by which layer owns the loop, state, permissions, and recovery.
The article distinguishes agent harnesses (OpenAI Codex, Claude Agent SDK), which own the execution loop, sandbox, permission model, and recovery; frameworks (LangGraph, OpenAI Agents SDK, Microsoft Agent Framework), which supply composable primitives; and MCP, a stateless JSON-RPC wire protocol governed by the Linux Foundation's Agentic AI Foundation since December 2025. An ownership matrix maps the execution loop, state, tool transport, permissions, recovery, sandboxing, and multi-agent orchestration to each layer. The 2026-07-28 MCP specification made the protocol fully stateless, retiring the initialize handshake and session headers.
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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.
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Programmable World Model
Programmable World Model decouples executable world-state evolution from video generation, reaching 94% Count Accuracy and 98% State Accuracy on new CombatStateBench.
An agent translates natural-language instructions into executable programs specifying entity states and transition rules, executed by a lightweight engine that maintains an explicit, persistent global world state including off-screen entities. State-augmented 3D oriented bounding boxes are deterministically compiled into pixel-aligned spatiotemporal conditioning signals for a pretrained video model acting as the generative renderer. On the new CombatStateBench benchmark it achieves 94% Count Accuracy and 98% State Accuracy, substantially outperforming existing interactive video world models.
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To See a World in a Living Context: Unified Indoor-Outdoor Urban World Generation
Researchers introduce HoloWorld, a unified text-driven framework generating coherent indoor-outdoor 3D urban worlds, improving average AQS over SOTA by 7.68%.
HoloWorld is a text-driven 3D generation framework that unifies indoor and outdoor urban world generation using a continuously updated cross-scale world context. It autoregressively generates urban exteriors with consistent spatial organization, grounded in 3D building instances and footprints, then produces building-specific interiors with geometry-constrained layouts that inherit exterior appearance. The authors claim it is the first framework to unify indoor and outdoor generation within one coherent 3D urban world, reporting a 7.68% average AQS improvement over prior SOTA and the highest average RDR score.
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Probabilistic Linear Explanations
Researchers introduce a unified probabilistic explainability framework using sparse anchored linear models that outperforms LIME and MAPLE on relevance error.
The paper proposes probabilistic explanations based on sparse, anchored linear models applicable to both binary classification and continuous regression. It proves that minimizing relevance error for neural-network models is NP-hard and relates it to a tractable fidelity-error surrogate. Solutions are computed via a mixed integer programming formulation with provably optimal empirical solutions and a polynomial-time iterative hard thresholding algorithm with approximation guarantees. Empirical evaluations show lower relevance error than LIME and MAPLE while satisfying anchoring and sparsity constraints by construction.
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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.
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PANORAMA: Panoptic Grounded Captioning via Mask Proposal Selection
Researchers introduce PANORAMA, a vision-language model grounding caption phrases in pixel masks, alongside the PanoCaps benchmark and gPQ metric.
The paper studies panoptic grounded captioning, requiring VLMs to describe foreground and background regions while grounding each phrase with pixel-level masks. The authors release PanoCaps, a human-annotated benchmark built from panoptic segmentation datasets, plus a phrase-mask matching protocol and a generalized Panoptic Quality (gPQ) metric. PANORAMA formulates grounding as selection from phrase-conditioned mask proposals generated by a pretrained segmenter, achieving the best overall grounding on PanoCaps and matching or exceeding specialized models. Code, data, and models are publicly available.
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LACE: Layer-Wise Compression for Dynamic Frame Rate Codecs
LACE introduces layer-wise compression for dynamic frame rate audio codecs, cutting sequence lengths and speeding TTS inference while preserving quality.
LACE (Layer-Adaptive Codec Encoding) applies an independent compression step at each quantization layer of a neural audio codec, enabling layer-specific segmentation boundaries instead of shared ones. Union alignment and boundary anchor mechanisms keep durations consistent for downstream text-to-speech. On LibriTTS, LACE achieves a better rate-quality tradeoff than prior dynamic frame rate codecs and improves TTS inference efficiency at competitive synthesis quality. Code is released in the ESPnet3 codec recipe.
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Reduced-Space Multi-Fidelity Bayesian Optimization of Process Simulation Models
RS-MFBO couples global sensitivity analysis with fidelity-augmented Gaussian processes to slash costly high-fidelity simulation runs in industrial flowsheet optimization.
The paper presents RS-MFBO, a reduced-space multi-fidelity Bayesian optimization framework for high-dimensional, expensive black-box functions. It integrates Global Sensitivity Analysis for dimensionality reduction with a fidelity-augmented Gaussian process and a cost-aware acquisition strategy featuring cooldown and promotion mechanisms. Validation on a plasmid DNA bioprocess (SuperPro Designer) and a green fuel synthesis plant (Aspen HYSYS) shows substantial reductions in high-fidelity evaluations while remaining competitive with single-fidelity baselines.
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Tracking the Unseen: An Occlusion-Robust Framework for Target Tracking Under Full and Long-Term Occlusion
Occlusion-robust tracker combining YOLOv11n, Kalman filtering, and appearance Re-ID cuts identity switches and beats OccluTrack by 18.1% MOTA on OVIS.
The framework integrates YOLOv11n object detection, Kalman Filter motion prediction, and occlusion-aware appearance-based re-identification to maintain target identity through full and long-term occlusion. Six Re-ID architectures were evaluated under identical conditions, with the Occlusion-Aware Mask Network (OAMN) performing best. On the public OVIS dataset it improves MOTA by 18.1% and IDF1 by 25.1% over OccluTrack while reducing identity switches by 12.8%; on a custom military surveillance dataset it achieves MOTA 0.734 and IDF1 0.729.
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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%.
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AI for Games in the Foundation Model Era
Survey organizes foundation-model AI for games into six roles and analyzes which capabilities transfer across playing, design, building, runtime adaptation, and testing.
A survey maps foundation-model and learned world-model research across the game lifecycle into six roles: playing/acting, modeling players and games, designing games, building/maintaining games, runtime generation/adaptation, and testing/evaluation. The authors identify cross-role connections such as trajectories training world models and design specifications driving executable implementations. Control schemes, rules, engine interfaces, state representations, and player contexts often remain setting-specific, so downstream claims require validation in the target setting. Evaluation is most standardized for bounded game playing, while persistent state, repeated revision, validated player modeling, and automated testing remain less established.
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Privacy-enhanced federated learning via asynchronous aggregation and local differential perturbation
Federated learning framework combining dynamic differential privacy, homomorphic encryption, and local DP retains 82.6% accuracy at epsilon 0.1 while cutting communication 21.3%.
The paper proposes a privacy-enhanced federated learning framework integrating Dynamic Differential Privacy, lightweight Homomorphic Encryption, and Local Differential Privacy during training. An asynchronous aggregation strategy with version control supports distributed training in asynchronous environments. On CIFAR-10 and Purchase-100, the method maintains up to 82.6% classification accuracy under stringent privacy constraints (epsilon = 0.1) and reduces communication overhead by 21.3% versus FedAvg.
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Task-Directed Residual AddUNet:Perfect-Reconstruction Routing for Full-Rate Representations
Paper recasts additive U-Net skip structure as a perfect-reconstruction filter bank and adds full-rate residual routing for task-directed representations.
The work proves a constrained additive U-Net's survivor-skip structure is exactly equivalent to a critically sampled perfect-reconstruction filter bank, and removes complementary-subband restrictions via a full-rate formulation. A Residual Full-Rate PR architecture routes task-irrelevant or redundant structure away from the task pathway while guaranteeing exact reconstruction without invertible operators, a matched synthesis bank, or a learned decoder. On TIMIT, the front-end improves test PER from 28.60±2.09% to 25.76±0.41% with recognizer and training held fixed.
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VC-Attention: Value Smoothing and Softmax Casting for Low-bit Attention
VC-Attention is a training-free low-bit attention method for diffusion transformers, achieving 1.46-1.59x kernel speedups on datacenter GPUs with higher fidelity.
VC-Attention is a training-free low-bit attention framework for diffusion transformers that pairs V-Smooth value smoothing via lightweight online clustering with ExpCast-FP8, which maps log-domain scores directly to E4M3 FP8 probability codes and eliminates the FP32 softmax exponential. It is implemented for B200, B300, H200, RTX PRO 6000, and RTX 5090 GPUs. Across Wan2.2, LongCat-Video, HunyuanVideo-1.5, and MiniMax-H3, it improves fidelity over low-bit baselines and speeds attention 1.46-1.59x over BF16 FlashAttention-4 on datacenter Blackwell and Hopper GPUs and 2.3-3.6x on workstation cards, with 1.13-1.70x faster end-to-end clip generation.
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HarnessVLN: Unifying Training-Free Embodied Navigation through an Agent Harness
Researchers introduce HarnessVLN, a zero-shot training-free agent harness that sets new training-free SOTA on vision-language navigation benchmarks including R2R and HM3D.
HarnessVLN is a zero-shot, training-free framework for embodied vision-language navigation that coordinates perception, retrieval, grounding, navigation, recovery, and termination through a unified tool interface. It validates planner proposals against spatial evidence, geometric feasibility, and subgoal consistency, using hierarchical event memory and a persistent Spatiotemporal Graph that stores reusable spatial evidence and failure annotations. It reports success rates of 60.8% on R2R, 53.9% on RxR, 76.0% on HM3D-v2, and 59.3% on HM3D-OVON, surpassing prior training-free state of the art, with real-world humanoid deployment demonstrated.
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Real-SWE: Benchmarking AI models on private, real-world, enterprise codebases
Real-SWE benchmark tests coding agents on licensed private enterprise codebases; top model Fable 5.1 resolves only 38.8% of tasks.
Real-SWE is a new benchmark evaluating frontier AI coding agents on tasks drawn from private production codebases licensed from real companies, spanning billing, tax calculation, and cross-service migrations. Fable 5.1 with Claude Code leads at 38.8% resolution rate (pass@1 over eight runs), followed by GPT-6 Astra Codex CLI at 33.8% and Gemini 3.8 Flash Gemini CLI at 31.2%. Tasks use native harnesses and realistic tooling including Docker, Kubernetes, PostgreSQL, Redis, and Linear; median reference solutions edit 11 files versus 6 for DeepSWE and FrontierCode.
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AlayaVista: Streaming World Modeling from Panoramic States to Perspective Video
AlayaVista is a camera-controllable streaming video world model that decouples panoramic scene evolution from perspective synthesis, trained on a 1,318-hour 4K dataset.
AlayaVista builds a 360-degree scene prior from a single perspective image, evolves it as a camera-conditioned panoramic latent state, and maps it to perspective video via a latent viewport renderer plus a perspective refiner. Chunk-autoregressive generation and few-step distillation enable efficient streaming. The authors introduce MUGEN, a real-world panoramic video dataset with 1,318 hours of at-least-4K video and rich semantic and geometric annotations.
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MCRL2: Multi-resource Cross-attention-based Representation Learning-augmented Reinforcement Learning for Cloud Microservice Scheduling
MCRL2 augments reinforcement learning with multi-resource cross-attention representations to improve cloud microservice scheduling and load balancing.
MCRL2 combines a multi-resource cross-attention representation learning module (MCRL) with an actor-critic architecture and maximum entropy objective for microservice scheduling. The approach captures interdependencies among nodes, resources, and microservices in data centers. Experiments on real production cluster traces show improvements in load balancing, scheduling success rate, and average completion time versus baselines.
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Groupoid-Based Internal State Representations for Reinforcement Learning with Local Symmetries
Groupoid-based RL discovers local, state-dependent symmetries during interaction, learning in a symmetry-reduced space and beating standard Q-learning efficiency.
The paper proposes a reinforcement learning framework using groupoids to capture local, state-dependent symmetries and dynamically discover equivalence structures during interaction. The agent maintains orbit representatives together with transporters that map raw states to canonical forms, enabling learning and decision-making in a symmetry-reduced space while preserving local distinctions. Empirical results show improved sample efficiency and convergence over standard Q-learning in dense and large-scale environments with strong partial symmetries.
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SNAP3D: Physically Grounded 3D Parts for Assembly from a Single Image
SNAP3D uses physics simulation feedback to make single-image 3D part generation produce valid, stable assemblies, validated through 3D printing.
The framework improves part-aware 3D generation by resolving inter-part penetration, recovering contact graphs between neighboring parts, and placing parameterized connectors at contact surfaces. Physical simulation feedback refines connector placement, orientation, and dimensions to improve assembly stability while preserving geometry. A physics-based evaluation protocol tests assembly validity and stability under gravity, and results are validated through 3D printing and real-world assembly.
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Memory as Plans: World-Action Modeling with Memory-Grounded Planning
Researchers introduce MaP-WAM, decomposing memory-dependent robot manipulation into memory-grounded planning and plan-conditioned execution, achieving 83.3% on RMBench and 78% on real robots.
MaP-WAM converts long-term multimodal episodic memory — segment records with language instructions and sparse visual context — into compact plans of next-segment language goals and visual guidance. A World-Action-Progress model jointly predicts action chunks and execution progress, calibrating predictions via plan-observation alignment for adaptive segment transitions and closed-loop context updates. Structured attention keeps the executor context length fixed and enables key-value caching, yielding state-of-the-art 83.3% success on RMBench, 78.0% on real-robot tasks, and roughly constant inference latency as task history grows.
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Beyond Solver Verdicts: Generative Reward Models for Autoformalization
Researchers introduce Generative Verification (GenV), a generative reward model achieving 0.961 AUROC in detecting unfaithful autoformalization that preserves solver verdicts.
The paper formalizes Verdict-Preserving-Unfaithfulness (VPU), a failure mode in neurosymbolic autoformalization where an incorrect encoding executes successfully and matches the expected solver verdict, and proves verdict-only verification is bounded to chance-level detection. The proposed Generative Verification (GenV) distills an offline Z3-equivalence oracle into a reference-free, continuous reference-equivalence score within the language model's vocabulary space. The oracle-mined verifier (GenV+HN) achieves 0.961 AUROC, generalizes zero-shot across unseen translators and formal styles, and yields an 11.3-point downstream accuracy gain in agentic test-time compute allocation. Mechanistic analysis with decision-projected logit lenses and sparse autoencoders shows the generative readout extracts precise spatial error coordinates without explicit localization training.
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Characterizing Language Generation in the Limit: Finite Witnesses and a Separation-Width Hierarch
New work characterizes language generation in the limit via finite witnesses, proves a full separation-width hierarchy, and formalizes all results in Lean.
The paper fully characterizes when language generation in the limit is possible for arbitrary families over a countable universe: each target must admit a finite positive witness such that targets activated by any finite sample share an infinite common intersection. It defines positive separation width and proves every level of the resulting hierarchy occurs, with countable families admitting singleton witnesses and unions of families with infinite common cores requiring unbounded finite witnesses. The characterization, a universal normalization, and a diagonal capture lemma are machine-checked in the Lean proof assistant, with the development maintained on GitHub.
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Forgetting Only What Matters: Layer-Selective Unlearning toward Robust LLMs
Researchers introduce FOM-UL, a layer-selective machine unlearning framework that improves forgetting-utility trade-offs and resists knowledge recovery after quantization.
FOM-UL selects transformer layers for unlearning using a forget-to-retain significance score, concentrating parameter updates on layers highly influential for the forget set while leaving most of the model unchanged. It reduces residual memorization versus GA, NPO, KLD, SURE, ReLearn, and LUNAR-based baselines on TOFU, KnowUnDo, and MUSE-style evaluations while preserving retain-set utility. Under 8-bit and 4-bit post-training quantization and adversarial prompts, it maintains stronger suppression of forgotten content, addressing brittleness of diffuse unlearning updates.
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Why Is Video Still So Expensive? A Survey of Inference-Efficiency Mechanisms in Video and Audiovisual LLMs
A survey catalogs inference-efficiency techniques for video and audiovisual LLMs, mapping bottlenecks in sampling, encoding, token reduction, and LLM decoding.
This survey covers inference-efficiency mechanisms for visual and audiovisual video LLMs, reporting reductions in parameters, FLOPs, latency, memory, and token counts. It organizes methods by pipeline stage, covering frame sampling, modality encoding, connector-level token reduction, and LLM prefilling and decoding for systems built since late 2022. The authors compile accuracy-cost comparisons under shared host models and input protocols, identify gaps in audiovisual efficiency and standardized evaluation, and maintain a public repository.
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When Does Scale-Invariant Optimization Become Unstable? An Exact Schedule Law with Weight Decay
Researchers derive an exact law linking learning-rate schedules and weight decay in normalized networks, pinpointing when scale-invariant optimization destabilizes.
The paper shows that normalization makes large parts of neural networks scale-invariant, creating a hidden feedback loop where learning-rate schedules and weight decay interact through the parameter norm to control the effective optimizer step. An exact discrete-time law with a single scalar quantity separates contraction- and expansion-dominated effective learning-rate regimes, and the balance point is intrinsically unstable, so constant learning rate with weight decay produces recurrent behavior instead of a stable equilibrium. A unified homogeneous-optimizer framework explains why adaptive methods stabilize more weakly under normalization. The law is validated with high precision on MLPs, CNNs, and GPT-2 across MNIST, CIFAR, WikiText, and OpenWebText, with code released on GitHub.
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Benchmarking Qwen3.8 27B quantizations: 4-bit holds up, 1-bit collapses
Quesma benchmarks Qwen3.8 27B quantizations: 4-bit Q4_K_M matches BF16 on key benchmarks while 1-bit collapses to random chance.
Quesma spent roughly $3,000 on Modal GPUs testing Unsloth GGUF quantizations of Qwen3.8 27B across GPQA Diamond, IFBench, and Terminal-Bench 2.1. The 17 GB Q4_K_M quantization matched the 55 GB BF16 model on Terminal-Bench 2.1 and showed little degradation down to 4-bit, while the 2-bit UD-Q2_K_XL dropped noticeably. At 1-bit, scores on GPQA Diamond fell to random-guess levels, with longer reasoning making results worse, and reasoning effort settings significantly affected outcomes.
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