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“Linear”

arxiv.orgpaper🧠 AI

Efficient Scalable Oversight via Rewinding

Rewindable interactive proofs (RIPs) are a new model of relativizing interactive proofs for scalable oversight, in which a weak verifier can rewind an AI prover to an earlier saved state. The paper builds an RIP for computations robust to changes in a small fraction of oracle answers. The honest prover runs in linear time, the verifier runs in nearly linear time in the input length after one-time preprocessing, and no cryptography is used. A preliminary MNIST experiment shows an RIP can be faster than a deployed proof system.

rewindable interactive proofsdebaterelativizing interactive proofs

arXivMNIST

research, ai-safety· Added 31 Oct 2026, published 29 Oct 2026

arxiv.orgpaper🛡️ Security

Understanding and Mitigating Role Spoofing in LLMs

Understanding and Mitigating Role Spoofing in LLMs is an arXiv paper (October 2026) on role spoofing, a prompt injection where malicious tool output impersonates the user. It shows models track "who said what" using both content cues (semantics, style) and structure cues (control tokens, formatting). Attacks combining fake structure and content succeed often, adversarial training stops only content-based attacks, and linear probes trained to predict conversation roles detect spoofing across settings.

LLM

role spoofingrole confusionprompt injection+1

AI Security· Added 31 Oct 2026, published 29 Oct 2026

arxiv.orgpaper🧠 AI

A Unified Hessian-Guided Framework for Post-Training Quantization

This preprint proposes a unified post-training quantization framework for large language models. It derives a Hessian-guided approximation of the output change caused by joint weight and activation quantization, using a tractable Fisher approximation. Quantization ranges, linear transformations and weight rounding are optimized together under one output-sensitivity objective. The authors report gains over state-of-the-art transformation and rounding methods in channel-wise and block-wise settings.

LLMpost-training quantizationHessian-guided approximation+1

arXiv

research, inference· Added 31 Oct 2026, published 29 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Quasi-polynomial time algorithms for block basis reduction of linear codes

Kolesnikov and Novoselov give block basis reduction (BKZ) algorithms for linear codes that need polynomial or quasi-polynomial numbers of calls to a minimum weight codeword oracle at fixed block size. The earlier Ghentiyala and Stephens-Davidowitz bounds are exponential. The paper adds a truncated BKZ and a DeepBKZ variant, with an implementation for block sizes 2 to 24 and approximation-factor estimates on random codes.

BKZDeepBKZminimum weight codeword problem+4

other· Added 31 Oct 2026, published 29 Oct 2026

arxiv.orgpaper🧠 AI

When Is Additional Retrieval Worthwhile? A Cost-Aware Study of Evidence Gain and Answer Reliability in RAG

This paper studies a cost-aware, two-action RAG routing policy that chooses between dense retrieval plus reranking and a hybrid lexical-dense retriever, using a regularized linear model to predict per-question evidence gain. On 60 held-out QASPER articles (188 questions), it triggers hybrid retrieval on 24.5% of questions and matches always-hybrid Hit@10 (78.70%). Answers from 4-bit Qwen3-4B show no clear correctness or faithfulness gain, and the assessments are model-assisted.

RAGQwen3BM25

QASPER

research, llm-apps, evals· Added 28 Oct 2026, published 27 Oct 2026

arxiv.orgpaper🧠 AI

AK_QMUL at ArGuard Shared Tasks: A Single Fine-Tuned JAIS for Arabic Harmful-Prompt Detection

AK_QMUL's system for the ArGuard-2026 shared tasks fine-tunes JAIS-6.7B with 4-bit QLoRA and a linear head on the last-token representation. For binary safe/unsafe detection (B1) it uses a four-way head over dialect register and AraSafe MSA augmentation, reaching macro-F1 0.984. For seven-way harm domain (B2) it averages five seeds, reaching 0.790. Both ranked first. Bully is the weakest class.

JAIS-6.7BQLoRALLM

JAISArGuardAraSafe+2

research, ai-safety, training· Added 28 Oct 2026, published 26 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Private Information Retrieval with Accountability at Erasure-Optimal Server Redundancy

SynTrace is a single-round compiler that adds accountability to linear private information retrieval (PIR). The client mixes the retrieval query with m audit queries, so audit syndromes identify up to d deviating servers among t colluding ones before reconstruction. The paper proves perfect query privacy and zero false identification within the threshold. For Shamir-based PIR it needs k=t+d+1 servers. For a 32 KiB record from a 1 GiB database it uses 2.25 MiB, against 8.96 MiB for the Eriguchi, Kurosawa and Nuida (TCC 2022) homomorphic-MAC construction.

SynTraceEriguchi-Kurosawa-Nuida homomorphic MACTCC 2022

other· Published and added 26 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Maximum-Likelihood Decoding of Gabidulin Codes Is NP-Hard Even in Dimension One

Philippe Gaborit proves that maximum-likelihood decoding of Gabidulin codes is NP-hard even for dimension one: deciding rank distance at most n-2 from the code {λg} is NP-complete. The reduction is deterministic, so Rank Syndrome Decoding and Rank Minimum Distance for F_{q^m}-linear codes are NP-complete under deterministic reductions, where the earlier reduction was randomized. It goes through a new pivot problem, Twisted Square Membership.

Gabidulin codes

Philippe GaboritGuruswami-Vardy theoremTwisted Square Membership+2

other· Added 26 Oct 2026, published 25 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Chosen Commitments and Certified Decoding Regions in the Adaptive Threshold Schnorr Attack

This IACR ePrint 2026/2632 paper by Demirtaş and Yayla analyses the Crites–Stewart adaptive forgery attack on threshold Schnorr signatures, which needs one call to a sparse linear search oracle (bounded distance decoding of a scaled Reed–Solomon code). It shows that choosing the commitment coefficient vector does not help much: a wrapper adversary with Q challenges succeeds with probability at most QM/p, even if its solvers decode every witness for free. For the six original (N, τ) pairs, M stays below 2^24, so the bound is under 2^-100 after 2^128 challenges at p = 2^252.

Schnorr signaturesReed–Solomon codesfolded Reed–Solomon codes

Crites and Stewart attackGuruswami–WangAshvinkumar–Habib–Srivastava+2

other· Added 26 Oct 2026, published 24 Oct 2026

x.comthread🧠 AI

Prasenjit Sarkar (@stretchcloud) on X

Post explaining Claude's new support for MCP Tasks and Elicitations. A server returns a durable task handle instead of blocking. The client polls tasks/get every 250 ms to 30 s, and states are working, input_required, completed, failed or cancelled. Elicitations pause a task at input_required for human approval. It also notes Cloudflare's stateless MCP servers and Code Mode, and Sentry and Linear adoption.

Model Context ProtocolClaude CodeGitHub Actions+3

AnthropicClaudeAWS+4

mcp, agents· Added 25 Oct 2026, published 24 Oct 2026

arxiv.orgpaper🧠 AI

Semantically Rich, Metrically Affect-Blind: A Geometric Blind Spot in Modern Text Embedders

Sentiment-Injected Sentence Embeddings (SISE) addresses a finding across eleven frozen text encoders (2021 to 2026): linear probes recover sentence polarity at up to 91.3% accuracy, but cosine similarity separates polarity by only 0.003-0.064. A small supervised-contrastive head (0.13M-0.69M parameters) trained on CPU raises retrieval MAP by up to 26 points, but the 128-d head loses 38-41 STS-B Spearman points. The paper also releases the Attribute Alignment Gap diagnostic.

Qwen3RAG

Qwen3-Embedding-4BJina-v5SISE+3

research, llm-apps· Added 26 Oct 2026, published 23 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Balsamic: One-round Threshold UOV from GPU-Accelerated FHE

Balsamic is a complete homomorphic Unbalanced Oil and Vinegar (UOV) signer, used to build one-round threshold UOV signatures from a packed discrete-CKKS FHE circuit. A paired-pivot linear solver and pivot-success certificates allow encrypted retry selection. In 2-out-of-3 semihonest signing, each online partial signature takes 10.7 s on an RTX 6000 Ada and 7.77 s on a GB300, with 5.25 MB per response.

UOVCKKSFHE+2

Balsamicuniversal thresholdizerspaired-pivot solver+1

other· Added 24 Oct 2026, published 22 Oct 2026

arxiv.orgpaper🧠 AI

Progress Probes $P^2$: Lightweight Runtime Monitoring for Vision-Language-Action Models

Progress Probes (P^2) trains linear probes on a vision-language-action model's own activations, using demonstrations, to estimate task progress, then flags executions that deviate from expected progress. Tested on the open-source pi0.5 across simulated and real-world manipulation tasks, it matches or beats VLM-based monitors and failure-data-trained methods at failure detection, without an external foundation model.

vision-language-action modelvision-language modellinear probes

pi0.5arXiv

research, multimodal· Added 24 Oct 2026, published 22 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Linear Cryptanalysis of TFHE-Friendly Cipher FRAST in the Average Key Setting

Linear cryptanalysis of FRAST, a TFHE-friendly stream cipher, in the average key setting. It fixes the false-positive problem left open in the earlier attack by Bak et al., showing key differences fall into five indistinguishable classes per round. The Contract algorithm recovers the master key for 10 rounds and, on weak keys of density 2^-11.8 to 2^-5.4, reaches full FRAST within the claimed security.

FRASTTFHE

Contract algorithmBak et al.linear cryptanalysis+1

other· Added 24 Oct 2026, published 22 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Efficient Laconic Oblivious Transfer with Malicious Security

Efficient Laconic Oblivious Transfer with Malicious Security is an ePrint paper (2026/2593, ASIACRYPT 2026 revision) giving the first maliciously secure laconic OT that uses its primitives only as black boxes. It adds equivocal vector commitments and maliciously secure witness encryption to the commitment-plus-witness-encryption paradigm. Security is proven in the UC model against malicious senders and in the context model against malicious receivers. Instantiations use LWE and bilinear Diffie-Hellman assumptions in the standard model.

laconic oblivious transferequivocal vector commitmentswitness encryption+3

other· Added 24 Oct 2026, published 22 Oct 2026

arxiv.orgpaper🧠 AI

Every Part Has Its Role: Part-of-Speech Effects in LLM Post-Training

Every Part Has Its Role studies concept augmentation in LLM post-training, where a target word is replaced by related alternatives in training sentences. It extends prior work from nouns to verbs and adjectives. Concept augmentation improves perplexity most consistently on news regardless of part of speech, while verbs give the most reliable downstream gains. CKA and linear probing show the largest gains do not match the largest representation changes.

LLM

arXiv

research, training· Added 23 Oct 2026, published 21 Oct 2026

eprint.iacr.orgpaper🛡️ Security

SoK: Design-Level Backdoors in Block Ciphers Constructions, Detection, and Key Recovery

SoK on design-level backdoors in block ciphers. It places four published designs (Rijmen and Preneel, Paterson, Bannier and Filiol, Peyrin and Wang) in one taxonomy and gives a sound algorithm that recovers a partition trapdoor from S-box tables. It attacks KG-DES, BEA-1 and LowMC-M: KG-DES key recovery takes minutes, and the LowMC-M MALICIOUS backdoor yields the key from 35 to 409 queries. Trapdoors hidden in linear layers leave no trace, so the authors recommend deriving constants verifiably from a public seed.

KG-DESBEA-1LowMC-M+2

Rijmen and PreneelPatersonBannier and Filiol+5

other· Added 24 Oct 2026, published 21 Oct 2026

arxiv.orgpaper🧠 AI

What Language Adds, and at What Cost: Four Controlled Probes of Pathology Vision-Language Models

This preprint grades evidence for pathology vision-language models and tests four claims with controlled probes on released checkpoints and public data. Stage-redacted report embeddings raise pooled C-index by 0.019 (progression-free interval) and 0.016 (overall survival) across 1,633 TCGA patients. Zero-shot prompting matches a linear probe. SlideChat scores 74.82% with the slide and 74.41% without it. Paraphrasing flips 8-23% of answers.

vision-language model

SlideChatSlideBench-VQATCGA

research, evals, multimodal· Added 21 Oct 2026, published 19 Oct 2026

eprint.iacr.orgpaper🧠 AI

Analysis of an AI based code distinguisher

The paper analyses DeepDistinguisher, a transformer-like model that tells Goppa codes apart from random linear codes. Saliency maps and attention visualisation reveal a low-weight-row artifact in the random training instances that the model partly relies on. A pairwise-XOR rule inspired by minimum distance correlates strongly with the model's predictions, and the model loses most of its power on random matrices meeting both constraints. This suggests it exploits general combinatorial properties, not hidden algebraic structure.

Transformer

DeepDistinguisher

research· Added 20 Oct 2026, published 19 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Analysis of an AI based code distinguisher

Analysis of DeepDistinguisher, a transformer model that tells Goppa codes from random linear codes. Using saliency maps and attention visualization, the authors find a low-weight-row artifact in the random training instances that the model partly uses. They propose a pairwise-XOR rule on generator matrix rows that correlates strongly with the model's predictions. On random matrices meeting the minimum distance and XOR constraints, the model loses most of its accuracy, so it exploits general combinatorial properties rather than hidden algebraic structure.

DeepDistinguisherGoppa Distinguisher problemGoppa code

AI Security, other· Added 20 Oct 2026, published 19 Oct 2026

arxiv.orgpaper🧠 AI

Tandem: Serving Hybrid Linear-Attention GUI Agents on Mobile NPUs

Tandem is a runtime for serving hybrid linear-attention GUI agents on mobile NPUs. It saves recurrent state where consecutive prompts diverge, keeps most prefill on the matrix unit, uses a verification kernel that reads each weight block once, and releases NPU power votes between bursts. On a Snapdragon 8 Elite it cuts per-step latency 30% for text screen descriptions and 16% for screenshots, cuts energy per text step by 33%, and lowers loaded-model standby power from 0.76 to 0.22 W.

SnapdragonNPUhybrid linear attention

TandemSnapdragon 8 ElitearXiv

inference, agents, research· Added 21 Oct 2026, published 19 Oct 2026

eprint.iacr.orgpaper🧠 AI

HERMES: High-Performance Private Decoding for Large Language Models

HERMES is a privacy-preserving inference (PI) system optimised for multi-token LLM decoding rather than prefill. It swaps high-round nonlinear protocols for compute- and bandwidth-heavy ones with fewer rounds, and adds a homomorphic-encryption matrix-multiplication protocol that keeps the KV cache encrypted on the server and uses grouped-query attention. On Llama 3.1 8B Instruct it cuts TPOT by up to 2.7× and total PI latency by up to 2.1×.

LlamaLLMTransformer+1

Llama 3.1 8B InstructHERMES

research, inference· Added 19 Oct 2026, published 18 Oct 2026

eprint.iacr.orgpaper🛡️ Security

GKS FHE: Security Analysis

"GKS FHE: Security Analysis" is a Cryptology ePrint Archive preprint (2026/2523, October 2026) giving a formal security analysis of the Gribov-Kahrobaei-Shpilrain private-key fully homomorphic encryption scheme. The abstract says the scheme needs no bootstrapping or de-linearization, gives bit-exact decryption, supports native encrypted search and has unbounded depth. The page shows only the abstract, so the analysis's methods and results are not visible.

GKS FHE

other· Added 19 Oct 2026, published 18 Oct 2026

arxiv.orgpaper🧠 AI

TileDRAM: Compiler-Controlled Tile-to-DRAM Layouts for 3D DRAM GPGPUs

TileDRAM is a compiler-runtime toolchain that adds tile-to-DRAM layouts as a compiler-visible abstraction for compute-unit-local 3D DRAM GPUs. It extends GPU Linear Layout to DRAM placement and picks layouts per tile access signature using row-buffer behavior. On 108 MoE FFN cases in Accel-Sim/Ramulator2, row-conflict rate fell from 75.7% to 15.2%, with a 1.77x geomean speedup. Overhead on an RTX 3090 is 0.17%.

Mixture of ExpertsCUDAAccel-Sim+2

TileDRAMRTX 3090

research, inference· Added 20 Oct 2026, published 16 Oct 2026

arxiv.orgpaper🧠 AI

Knowledge That Is Linear Before Fine-Tuning Generalizes After It

The paper separates "active" facts (which support generalization beyond recall) from "inert" ones. It builds new relations by relabeling answers of existing ones (Paris -> Europe becomes Paris -> crab), fine-tunes on a subset of subjects, and tests held-out subjects that share an answer (Munich -> crab). Across eleven language models and 70 relations, linear readability of the original relation in pretrained representations correlates with this generalization at Pearson r = 0.87 to 0.92 within each model. The result holds after controlling for fine-tuning set size, original-relation accuracy and subject frequency in pretraining text, and it survives re-randomized labels, seeds and thirteen other label categories. Mapping wrong labels back to the original answers shows the fine-tuned model's errors follow the linear probe's top answers.

LLM

arXiv

research, training· Added 17 Oct 2026, published 15 Oct 2026

arxiv.orgpaper🧠 AI

The importance of tensor handling and implementation of physics-informed DeepONets

Proposes a modified Physics-informed DeepONet (PI-DeepONet) architecture that removes redundancy in how the Branch network processes input functions across collocation points. It is mathematically equivalent to the original but uses smaller input tensors and fewer Branch evaluations. The authors derive training-time scaling laws, then test on 1D, 2D and 3D linear advection problems. Accuracy matches the original, the maximum speed-up is 3.75× in 2D, and a 3D model trains on an NVIDIA A100 only with the new formulation, because the original runs out of memory.

PI-DeepONetDeepONetPINN+1

NVIDIAarXiv

research· Added 17 Oct 2026, published 15 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Connecting Cryptanalytic Distinguishers through Divisibility

Paper that links linear, integral, differential-linear and restricted differential distinguishers through two-way conversions with explicit divisibility guarantees and data complexity. It gives automated MILP/SAT models (Walsh and ultrametric representations, exact mix-basis models) and a black-box FWHT histogram search for zeros and finite divisibility. It improves Beyne–Verbauwhede divisibility bounds in 674 SIMON cases (e.g. 2^5 to 2^11 on 9-round SIMON32) and PRESENT. It also gives first higher-divisibility proofs for 8-round SPECK and BEANIE, new bounds for GIFT-64 and Ascon, and new properties on RC5, ChiLow-32, KeeLoq and KATAN32.

SIMONSPECKPRESENT+3

Hosein HadipourBeyne and VerbauwhedeBEANIE+4

other· Published and added 14 Oct 2026

arxiv.orgpaper🧠 AI

RepProbe: Can Video Language Models Count Repeated Actions? A Diagnostic Benchmark

RepProbe is a benchmark of 42K real and synthetic video clips with exactly 1–20 complete action repetitions, controlling for clip position, temporal coverage and duration. Video language models' counting accuracy collapses at high counts, and this is not explained by frame sampling alone. Linear probes on final-layer representations show count information stays linearly decodable even when the model's answer is wrong, so the failure arises at the output stage, where answers cluster on a few preferred numbers.

vision-language model

RepProbearXiv

evals, multimodal, research· Added 14 Oct 2026, published 13 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Leakage-Resilient Incompressible Cryptography: Constructions and Barriers

Introduces leakage-resilient incompressible cryptography, a security model combining leakage resilience (partial key leakage plus full ciphertext) with incompressibility (full key but only part of the ciphertext or signature). It gives a transformation from incompressible symmetric-key encryption (SKE) to leakage-resilient incompressible SKE, a leakage-resilient incompressible PKE built from a new leakage-resilient non-committing KEM, and an incompressible SKE with ciphertext and compression rate 1/2 using invertible extractors. It also gives a leakage-resilient incompressible signature scheme in the random oracle model under DCR or LWE plus Decisional Linear. Black-box impossibility results show barriers to improving these parameters.

DziembowskiDecisional Linear assumptionLWE+3

other· Added 14 Oct 2026, published 13 Oct 2026

arxiv.orgpaper🛡️ Security

AgentValve: Sybil-Resilient Payment Authorization for Permissionless AI Markets

AgentValve is a protocol for authorizing payments by AI agents in permissionless markets where adversaries can cheaply create Sybil identities and fake feedback to inflate provider reputation. It models reputation as finite capacity in a directed trust graph, and a smart contract atomically reserves capacity along trust paths for each admitted payment. This bounds honest agents' exposure to the capacity they extend into the adversarial region, and payment-backed capacity creation with bounded route length limits depletion attacks. A witness-commit mechanism makes on-chain route verification linear work with constant persistent state per payment. The prototype was evaluated on six months of Ethereum data and synthetic workloads with up to 10,000 Sybil identities. Exposure and depletion stayed bounded, admission cost fell by up to 40.8% against baselines, and common payments cost about $0.04–$0.21.

Ethereum

AgentValveSybil attackcapacity depletion attack

AI Security· Added 12 Oct 2026, published 9 Oct 2026

arxiv.orgpaper🧠 AI

AgentValve: Sybil-Resilient Payment Authorization for Permissionless AI Markets

AgentValve is a protocol for Sybil-resilient payment authorization when AI agents pick and pay service providers without human review in permissionless markets. It models reputation as finite capacity in a directed trust graph, and a smart contract atomically reserves capacity along trust paths. This bounds honest agents' payment exposure to the capacity they extend into the adversarial region, so fake identities and fabricated feedback inside that region cannot raise the bound. Payment-backed capacity creation and bounded route length limit depletion by malicious senders. A witness-commit mechanism splits off-chain route discovery from on-chain verification, cutting verification to linear work with constant persistent state per payment. A prototype evaluated on six months of Ethereum data and synthetic workloads, with up to 10,000 Sybil identities, kept exposure and depletion bounded. Admission cost fell by up to 40.8% versus baselines, at roughly $0.04-$0.21 per common payment.

Ethereumsmart contracts

AgentValve

research, agents· Added 13 Oct 2026, published 9 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Revisiting Lattice-based Blind Signatures Again

This IACR ePrint paper by Yi-Fu Lai and Yu Yu breaks the blindness property of a lattice-based blind signature scheme presented at Crypto 2020, which uses a linear hash function framework instantiated over lattices. The authors first identify an overlooked bug in the scheme's original blindness security proof, then construct a concrete attack under an honest-key, honest-but-curious signer model by exploiting correlations between leaves of the scheme's tree structure, and discuss how the construction can be repaired.

Yi-Fu LaiYu YuCrypto 2020

other· Added 8 Oct 2026, published 7 Oct 2026

arxiv.orgpaper🧠 AI

TR-PTQ: High-Accuracy Integer-Only Transformer Post Training Quantization via Taylor Region Reformulation

TR-PTQ is a post-training quantization method for transformers that identifies learned LayerNorm scale parameters and GELU's compounded approximations (not SoftMax) as the main sources of accuracy loss when quantizing, contrary to common assumptions about numerical precision. It introduces a unified integer-only formulation using shared Taylor Region exponential/logarithm primitives so division and square roots can run entirely in the log-domain with standard integer arithmetic, paired with a calibration-free, outlier-aware LayerNorm optimization, eliminating the need for floating-point hardware for nonlinearities while keeping accuracy degradation under 1.5% across vision and language benchmarks.

Transformer

inference· Added 8 Oct 2026, published 7 Oct 2026

eprint.iacr.orgpaper🛡️ Security

A Universal Forgery Attack on the Origami Signature Scheme from the Public Key Alone

This IACR ePrint paper breaks Origami, a multivariate post-quantum signature scheme submitted to the NGCC round-1 public-key call, showing its bilinear construction is effectively a Rainbow-type scheme whose central map leaks through the public verification key in a predictable coordinate order. The authors derive an equivalent secret key from the public key alone, enabling universal forgery at roughly the cost of one signature verification (~0.01s at Origami-128), and demonstrate the attack against the unmodified reference implementation across all four parameter sets and the official test vectors. They also refute Origami's security proof by breaking its inversion assumption in a bilinear independent-coefficient model, and note a separate structural flaw where honest signatures lie in a proper subspace rather than the full space the specification assumes.

OrigamiRainbowUOV+1

other· Added 8 Oct 2026, published 7 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Settling Conjectures on Linear Structures of Inverse ChiChi Generalizations Four Proofs and a Counterexample

An IACR eprint paper proving four of five open conjectures, and disproving the fifth with a counterexample, from Andreoli and colleagues at ToSC 2025 about the linear height and component linear nullity of the inverse of ChiChi, the low latency nonlinear core used in the ChiLow cipher from Eurocrypt 2025 by Belkheyar and colleagues. The authors settle these bounds for all admissible branch lengths of the SWAP and 4-cycle generalizations, derive design consequences for ChiLow, and correct an erroneous data value from the prior work.

ChiLow

ChiChiSWAP4-cycle family

other· Added 8 Oct 2026, published 6 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Truffle: Maliciously Secure Three-Party Shuffles with Applications to Parsing

Truffle is a new three-party maliciously secure shuffle protocol for secure multiparty computation (MPC), matching semi-honest round complexity while adding only sublinear communication via a distributed-verifier zero-knowledge proof checking shuffle correctness. Authors Nidhish Bhimrajka, Yashvanth Kondi, Daniel Noble, and Bhavish Raj Gopal implement and benchmark it, applying it to securely parsing secret-shared strings, with further applications to graph analytics, anonymous communication, Distributed ORAM, aggregate statistics, and secure sorting. Published at ACM CCS 2026.

TruffleNidhish BhimrajkaYashvanth Kondi+2

other· Added 8 Oct 2026, published 6 Oct 2026

arxiv.orgpaper🧠 AI

Learning What to Imitate: Entropy-Aware Distribution Mixing

The paper proposes Entropy-Aware Mixing, a knowledge-distillation method where a small student model's per-token distribution is dynamically interpolated with a stronger teacher model's distribution, gated by the student's own predictive entropy, to avoid "confident conflicts" where the student must imitate low-probability continuations despite being confident in a different one. The authors implement both convex and geometric interpolations under two training regimes — offline trace generation via speculative decoding followed by SFT, and on-policy forward-KL distillation — and show it stabilises distillation, improving in- and out-of-distribution math reasoning while better preserving general capabilities than fixed-teacher supervision, with offline traces favouring concave entropy schedules and on-policy training favouring linear/convex ones.

training· Added 6 Oct 2026, published 5 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Optimized NEON Implementation of MAYO on Apple M1

An IACR ePrint paper presenting an optimized ARMv8 NEON assembly implementation of MAYO Round 3 (one of NIST's post-quantum signature candidates) for Apple M1, generated to be byte-identical to upstream intrinsics but 2–2.6x faster for signing and verification across all four parameter sets. The authors also run a constant-time testing campaign with matched null runs and find that while linear-algebra operations are statistically indistinguishable from constant-time nulls, verification (on public data) exhibits a measurable key-fixed timing effect present in both their implementation and the upstream code.

ARM NEONApple M1AES

MAYO

Hardware & RF· Added 8 Oct 2026, published 5 Oct 2026

arxiv.orgpaper🧠 AI

UniWAM: Unified World-Action Model

UniWAM is a unified architecture that combines a physical reasoner, a world generator, and an action predictor to jointly learn semantic understanding, visual generation, and action prediction for embodied agents, bridging vision-language-action (VLA) models and world-action models. It uses a data cleaning/annotation pipeline for human egocentric and robot data, represents low-level actions in natural language for VQA/egocentric/robot co-training, and introduces future visual noise augmentation plus history-conditioned flow matching to cut denoising steps while achieving state-of-the-art results on in-distribution performance, robustness, generalization, instruction following, and long-horizon tasks. The paper also reports a log-linear scaling law for unified human-robot co-training.

vision-language modelflow matching

UniWAM

research, multimodal· Added 2 Oct 2026, published 1 Oct 2026

arxiv.orgpaper🧠 AI

STEPQuant: When and Where Errors Matter in Delta-Rule Recurrent State Quantization

STEPQuant is a spatial-temporal post-training quantization framework for the fixed-size recurrent states used by Delta-rule linear attention, which become a memory bottleneck under concurrent serving. It allocates bit precision based on error magnitude and memory lifetime (temporal) and fits per key-row/value-column scales based on state distribution and output impact (spatial), achieving near-FP32 accuracy at a 6-bit budget and beating uniform INT8 at 4-bit. Evaluated on Qwen3.8-27B and Kimi-Linear-48B-A3B-Instruct, and integrated into SGLang with custom GPU kernels, it delivers over 5x recurrent-state compression and up to 68.7% total serving memory reduction.

SGLangCUDA

Qwen3.8-27BKimi-Linear-48B-A3B-Instruct

inference, research· Added 30 Sep 2026, published 29 Sep 2026

arxiv.orgpaper🧠 AI

LeapQuant: Efficient Linear Attention with Accurate Recurrent State Quantization

LeapQuant is a training-free quantization method for the fixed-size recurrent state used by linear-attention LLM architectures like Gated DeltaNet and Kimi Delta Attention, aiming to cut the inference bottleneck of repeatedly reading and updating that state. It introduces per-window quantization (re-quantizing the state only once per token window, using high-precision buffered updates in between) plus "Compensator Tokens" that preserve the state's largest outlier values in high precision, with residual smoothing to further reduce error. Across Qwen, Kimi, and GLM model families, it reaches near-FP32 accuracy at 8-bit while delivering 2.05–3.70x kernel-level and 1.47x end-to-end speedups on NVIDIA B200, RTX PRO 6000, and RTX 5090 GPUs.

Qwen

Gated DeltaNetKimi Delta AttentionKimi+2

inference, research· Added 30 Sep 2026, published 29 Sep 2026

arxiv.orgpaper🧠 AI

From Routing Signals to Selective Review: Visual regrounding in MoE VLMs

This paper shows that internal expert-routing probabilities in Mixture-of-Experts vision-language models (Qwen3-VL-30B-A3B-Instruct and Gemma-4-26B-A4B-it) carry a strong, low-cost signal for detecting when a model is about to answer a question about an object that isn't actually in the image (a 'target-absence grounding failure'). Linear detectors trained on target-token routing probabilities achieve ROC-AUCs of 0.9988/0.9956 on GQA-Inpaint (retaining 0.78-0.81 on the external OBER dataset), and gating a target-aware review prompt on these detections improves end-to-end accuracy by up to +22% without modifying model weights. The work matters for practitioners building on MoE VLMs because it demonstrates a weight-free, pre-generation hallucination detector that reuses computation the model already produces.

Mixture of Expertsvision-language modelQwen3-VL+1

GQA-InpaintOBER

research, multimodal· Added 30 Sep 2026, published 29 Sep 2026

arxiv.orgpaper🛡️ Security

When One Leak Pays Forever: Context Binding and the Price of Deterring Collusion

This arXiv paper models collusion in threshold-encrypted mempools, where a quorum of a decryption committee can sell its decryption capability to a front-runner and profit repeatedly as long as the leaked key keeps decrypting future blocks. The authors derive that deterrence requires a penalty (e.g., slashable stake) covering the largest discounted value a single leak can reach, show constant penalties cannot deter unbounded reuse without key rotation, formulate the cheapest deterring stake profile as a covering linear program, and argue per-epoch keys drastically cut the required stake versus a key valid for its whole lifetime. They calibrate the model on Ethereum front-running data and place the Ferveo and Shutter threshold-encryption protocols within it, with extensions to sealed-bid auctions, multi-authority voting, and federated learning under a shared key.

EthereumFerveoShutter

maximal extractable value

Blockchain· Added 2 Oct 2026, published 29 Sep 2026

arxiv.orgpaper🧠 AI

Copy the Same, Distill the Difference: Initializing Linear Vision Transformers

This paper studies how to initialize efficient linear-attention Vision Transformers from pretrained Softmax ViT weights instead of training from scratch. It finds that attention weights are operator-specific and don't transfer well by direct copying (distillation with a tailored loss is needed to recover token-routing behavior), while MLP weights are operator-agnostic and can simply be copied, and combining copied MLPs with distilled attention lets linear ViTs match or surpass Softmax ViT performance across model sizes and datasets.

Transformervision-language model

Vision Transformer

research, multimodal· Added 29 Sep 2026, published 28 Sep 2026

huggingface.copaper🧠 AI

Block Sparse Attention with Log-Linear Complexity (PISA)

PISA is a block-sparse attention mechanism that replaces the quadratic block-selection step of conventional sparse attention with a pyramid Top-K strategy: keys are organized into a coarse-to-fine hierarchy of O(log N) levels built via pooling, and LogSumExp scoring narrows candidates level by level down to the finest granularity, giving overall O(N log N) complexity instead of O(N^2). The authors implement hardware-aware Triton kernels for both training and inference that fuse hierarchical routing and scoring without ever materializing the full query-key score matrix, and show on language modeling benchmarks that PISA matches baseline performance on commonsense reasoning while improving results on retrieval tasks.

TransformerCUDA

PISA

research, inference· Added 28 Sep 2026

arxiv.orgpaper🧠 AI

Verifier Errors in RLVR: Reward Hacking, Limits of Feedback, and Selective Control

This arXiv paper analyzes reward hacking in reinforcement learning with verifiable rewards (RLVR), using gradient flow analysis with a fixed verifier to characterize when reward increases while correctness decreases. The authors prove that standard RLVR training signals are insufficient to detect or reduce "accepted errors" (incorrect responses rewarded by the verifier) without also suppressing correct responses, and propose a "selective control" correction that incorporates partial audit feedback on correctness to simultaneously lower accepted-error probability and raise correct-response probability. Experiments on log-linear and neural contextual bandits and a language model support the theory, showing selective control under partial auditing reduces accepted errors while increasing correctness.

training, research· Added 29 Sep 2026, published 28 Sep 2026

arxiv.orgpaper🧠 AI

LLM Inference in a Flash!

This paper presents algorithmic techniques enabling LLM inference on Compute-in-Flash (CIM) devices, which lack floating-point support and have limited write endurance. It introduces an end-to-end integer-only quantization pipeline (INT8 linear layers, integer Softmax/RMSNorm approximations) and a dictionary-based KV-cache compression scheme using sparse dictionary coding to cut KV write traffic by 15x. Evaluated on Llama-3.1-8B and Qwen-2.5-7B, the combined approach yields 3.1x-4.4x latency and 2.7x-6.8x energy savings over DRAM-based systems, particularly for long-context inference.

Compute-in-FlashINT8 quantizationKV cache

Llama-3.1-8BQwen-2.5-7BUC Berkeley+16

inference, research· Added 26 Sep 2026

arxiv.orgpaper🧠 AI

Control Under Compression: Reliability Frontiers for Tool-Using Agents

Introduces CompressAgent, a benchmark evaluating how compressing agent control contexts (system-side instructions specifying tools, arguments, policies, and recovery protocols) affects tool-using agent reliability. Testing nine ACCs, three task families, three Qwen models, and six retained-context budgets across 15,525 runs, it finds a nonlinear reliability frontier: compression stays safe near 75% retained context but reliability collapses sharply below 50%, with executable protocols becoming fragile at 10-25% and failures manifesting as tool-execution and action-parsing errors.

Qwen

CompressAgent

evals, agents· Added 26 Sep 2026

arxiv.orgpaper🧠 AI

LLM Inference in a Flash! (UC Berkeley)

This UC Berkeley paper designs LLM inference algorithms for Compute-in-Flash devices, which move computation into SSD memory to overcome bandwidth limits but lack floating-point support and have limited write endurance. It presents an end-to-end integer-only quantization scheme and a dictionary-based KV cache compression method (sparse dictionary coding representing KV vectors as linear combinations of static dictionary vectors), reducing dynamic KV cache traffic 15x on Llama-3.1-8B and Qwen-2.5-7B with limited accuracy loss.

Llama-3.1-8BQwen-2.5-7BUC Berkeley

inference, research· Added 26 Sep 2026

arxiv.orgpaper🧠 AI

Your Transformer Can Hold Two Thoughts at Once: Evidence of Linear Superposition in LLMs

This paper introduces the "Superposition Linearity Hypothesis": when inputs from two distinct text streams are linearly combined, an LLM's output approximates a superposition (average) of the two individual next-token distributions. The authors show this linearity is an intrinsic property of the Transformer architecture that weakens during pretraining but can be restored via lightweight fine-tuning, and they propose a guided decoding method that exploits this to generate two coherent continuations from a single forward pass.

Transformer

research· Added 26 Sep 2026

arxiv.orgpaper🧠 AI

BeatGraph: Self-Supervised Heartbeat Graphs for Infant ECG Representations from the Home Environment

BeatGraph is a self-supervised ECG representation model that tokenizes signals at the level of individual heartbeats rather than fixed-length patches, using a shared beat encoder, a Transformer for temporal ordering, and residual graph attention layers to relate beats within a 30-second window, pretrained via masked-beat embedding prediction. Evaluated on a new 3,408-hour, 143-infant home/classroom/lab ECG corpus, it improves macro-F1 over baselines by 0.076–0.158 on sleep-wake, infant-state, activity-source, and affect tasks, and transfers well to the pediatric ZZU-pECG benchmark (0.892 AUROC) and adult PTB-XL under linear evaluation despite infant-only pretraining.

Transformer

BeatGraphZZU-pECGPTB-XL+1

research, multimodal· Added 28 Sep 2026, published 25 Sep 2026

arxiv.orgpaper🧠 AI

Towards Practical Compression of 3D Gaussian Splatting

COSA-GS is a compression method for 3D Gaussian Splatting (3DGS) that builds context without spatial aggregation, using anchor-wise causal factorization: a compact learnable anchor latent derived from each anchor's geometry is fused with geometry context to drive attribute coding through a simple linear-transform architecture. The method uses rate-distortion optimization with adaptive Gaussian pruning plus quantization-aware training and integer inference so entropy-decoded symbols are bit-exact across platforms, addressing a practical failure mode (numerical inconsistency causing decode errors) in prior spatial-context compression approaches while achieving state-of-the-art compression rates. Code is released alongside the paper.

3D Gaussian Splatting

COSA-GS

multimodal, research· Added 25 Sep 2026, published 24 Sep 2026

arxiv.orgpaper🧠 AI

PoEM: Predicting RL Outcomes from Existing Policies

PoEM predicts the outcome of RL post-training on a new reward function using models already trained on other rewards, without running additional RL. It shows that when a new reward is a linear combination of existing ones, the resulting log-policy is a corresponding linear combination of existing log-policies, and even for non-linear reward combinations, log-policies often span a low-rank subspace whose combination weights can be estimated from sample outputs alone. Validated on synthetic and real rewards across text and image modalities.

PoEM

training, research· Added 26 Sep 2026, published 24 Sep 2026

arxiv.orgpaper🧠 AI

Practical and Space-Efficient LZ77 and LZ Pre-Compression via String Synchronizing Sets

This paper presents the first practical implementation of exact and 3-approximate LZ77 factorization using string synchronizing sets, building on Ellert's sublinear-space algorithms (SPIRE 2023) but replacing two previously impractical components (a degenerate lookup table and an orthogonal range reporting structure). The result runs in space close to the text size rather than a full suffix array, factorizing 12-19x faster than classical LPF on one thread with 14x less memory, and 1.4-2.9x faster than parallel LPF on 32 threads with 9x less memory. As a side result, feeding only its "perfect phrases" to a downstream compressor yields a precompressor competitive with the state of the art in compression ratio, memory, and parallel throughput.

EllertLZ77SPIRE 2023+2

research· Added 26 Sep 2026, published 24 Sep 2026

huggingface.coarticle🧠 AI

Training a coding model to paint watercolours with TRL and OpenEnv

A Hugging Face engineering blog post reproducing Surya Narreddi's viral "AI paints watercolours" project in the open: a coding LLM (Qwen3.5-35B-A3B, LoRA) is trained with GRPO via TRL to write p5.brush JavaScript sketches that render as watercolour flower paintings, using an OpenEnv RL environment with headless-Chromium rendering, an HPSv3 preference-model reward and a Qwen3-VL-30B-A3B-Instruct pairwise judge scored against a hand-curated 178-image reference pool. It documents the full pipeline (HF Jobs, Spaces, Inference Providers), the LoRA fix needed for MoE models (all-linear targeting), infra bugs found (an OpenEnv websocket caching bug fixed upstream, reward-noise from silent render failures), three reward-mix ablations, and open-sources the dataset, environment, training scripts, rollouts and trained models.

TRLOpenEnvHugging Face+8

Surya NarreddiHPSv3Qwen3.5-35B-A3B+12

training, multimodal· Added 29 Sep 2026, published 3 Sep 2026

arxiv.orgpaper🧠 AI

Overcoming the Randomness-Utility Trade-off in Answering Differentially Private Linear Queries

This arXiv preprint presents a randomness-efficient variant of the Hardt-Talwar K-norm mechanism for answering d linear queries under differential privacy, achieving O(d/ε) error in ℓ∞-norm using only O(log d) random bits. It improves on prior randomness bounds from Canonne et al. and Ghentiyala, and includes a computationally efficient version with a logarithmic increase in error, advancing the theory of differentially private query answering.

Hardt and TalwarCanonne et al.Ghentiyala

research· Added 22 Sep 2026, published 2 Sep 2026

arxiv.orgpaper🧠 AI

Learning CNN Filters via Generalized Stein's Method

This arXiv paper formulates CNNs as a generalized index model with matrix-valued input, treating convolution filters as index vectors, and proposes an SVD-based estimator built on a generalized first-order Stein's formula to learn those filters directly rather than via gradient descent. The authors show the estimator achieves an optimal convergence rate comparable to generalized linear models with known link functions, and report that it outperforms standard deep learning optimizers like Adam in simulations and real-data experiments; they also note the method generalizes to nonlinear dimension reduction and representation learning.

CNNAdam

research· Added 22 Sep 2026, published 2 Sep 2026

deepmind.googlearticle🧠 AI

Introducing Gemini 3.8 Flash and 3.8 Flash Cyber

Google DeepMind announces Gemini 3.8 Flash, an upgraded reasoning/coding model at the same $0.75/$3.75 per-million-token price as 3.7 Flash, plus Gemini 3.8 Flash Cyber, a specialized variant for vulnerability discovery and automated patching offered to vetted defenders through a new 'Fairwind Program'. The post cites benchmark gains on DeepSWE v1.1, HLE-Verified, Vals Finance Agent V2 and Harvey's Legal Agent Benchmark for the general model, and CyberGym plus Collinear's CWE-Bench for the cyber variant, along with real-world results from Chrome Security, Wiz and Google's Cloud Vulnerability Research team.

Gemini

Google DeepMindGoogleDeepSWE v1.1+15

models, evals· Added 29 Sep 2026, published 2 Sep 2026

deepmind.googlearticle🛡️ Security

Introducing Gemini 3.8 Flash and 3.8 Flash Cyber

Google DeepMind announces Gemini 3.8 Flash and a gated variant, Gemini 3.8 Flash Cyber, the latter positioned as a frontier-level model for autonomous vulnerability discovery and automated patching, accessible to vetted defenders through a new 'Fairwind Program.' On the CyberGym benchmark and an internal 20-language vulnerability-discovery benchmark it reports over 70% success, and on the Collinear CWE-Bench patching benchmark it nearly matches a larger frontier model (47.2% vs 47.8% pass@1) at much lower cost; Google cites Chrome Security producing 2.6x more correct patches and Wiz seeing higher recall at lower cost using the model, plus a case where Google's Cloud Vulnerability Research team found a critical vulnerability in under 2 hours.

GeminiChromeWiz

CyberGymCWE-BenchFairwind Program+5

AI Security, AppSec· Added 29 Sep 2026, published 2 Sep 2026

github.comrepo🛡️ Security

0sec-labs/0

"0" is an open-source security workflow runner from 0sec Labs (YC-backed, with a Swiss Applied AI & Cybersecurity Research Lab) usable from a terminal, a browser "Command Center," or a coding agent via MCP. It ships 12 starter templates (e.g. a "security-research" template that produces a source review of a demo API with code locations and suggested fixes), supports scheduled/triggered runs with retained results and revision history, and integrates with GitHub, Elastic, Semgrep, Snyk, Linear, Jira, Cloudflare, Slack and Teams via plugins.

Model Context ProtocolBunReact+1

FoxGuard

AI Security, AppSec· Added 2 Oct 2026, published 19 Aug 2026

blog.cloudflare.comarticle🧠 AI

MCP spec 2026-07-28: the stateless revolution (Cloudflare)

Cloudflare explains the MCP 2026-07-28 specification, which removes the mandatory stateful session handshake (Mcp-Session-Id) from the core protocol so MCP servers can run as ordinary stateless HTTP workloads (e.g. a single Cloudflare Worker) instead of requiring sticky sessions or Durable Objects. It covers the new Multi Round-Trip Requests (MRTR) model replacing stream-based elicitation, new Mcp-Method/Mcp-Name HTTP headers for gateway-level routing and caching, tightened authorization (CIMD preferred over DCR, RFC 9207 issuer checks, RFC 8707 audience binding), a formal Active/Deprecated/Removed feature lifecycle, and the createMcpHandler API graduating into the official TypeScript SDK, with testimonials from Sentry, Linear, and Anthropic's MCP lead.

Model Context ProtocolTypeScriptPython+6

AnthropicSentryLinear+4

mcp· Added 2 Oct 2026, published 6 Aug 2026

github.comrepo🛡️ Security

thomasbuilds/Spectre

Spectre is a free, open-source Android app (min API 31) for observing and interacting with the surrounding wireless environment, combining passive monitoring of Bluetooth LE, Wi-Fi, cellular (2G-5G), and GNSS with a growing set of pentesting tools. Notable features include a BLE GATT inspector that can read/write characteristics on connected devices, a local-network scanner (TCP-connect host discovery, SSDP/UPnP, mDNS/DNS-SD, banner grabbing, reverse DNS), iBeacon broadcast spoofing, and a computed aggregate RF-exposure figure derived by correctly summing per-emitter power in linear watts rather than dBm. It also documents Android platform limitations affecting wireless recon, such as invisible 5G NSA signal, Wi-Fi scan throttling, and single-carrier cell visibility.

Android

GATTSSDPUPnP+4

Mobile Security, Hardware & RF, Offensive Security· Published and added 23 Jul 2026

github.comrepo🧠 AI

magnus919/agent-skills

A sprawling open-source collection of 100+ reusable "Agent Skills" (markdown instruction packs with YAML frontmatter following the open Agent Skills format) for agentic systems, compatible with Claude Code and any framework that supports the standard. Skills span multi-agent orchestration frameworks (AutoGen, CrewAI, LangChain, LangGraph, LlamaIndex, DSPy, Haystack, a PydanticAI-based multi-agent debate tool), production agent operations, evals, and AI governance, alongside dozens of narrower domain skills — CLI integrations for Jira, Confluence, Notion, Linear, HubSpot, Ghost, Fireflies, Grafana, PostgreSQL, Kubernetes, Docker Compose, FFmpeg, Ghidra-based binary triage, FPGA/ESP32 hardware design, and more. The page text was truncated at 40,000 of 71,286 characters, so later entries in the catalog are not reflected here.

CrewAILangChainLangGraph+17

PydanticGraphMicrosoftStanford University+5

coding-agents, prompting· Added 1 Oct 2026, published 11 Jul 2026

soliditylang.orgadvisory🛡️ Security

Solidity 0.8.36 Release Announcement

Solidity 0.8.36 release notes disclose two medium-severity compiler bugs: a Yul optimizer call-cycle detection flaw that let stack-to-memory spilling be wrongly applied to mutually recursive functions, causing separate invocations to share and overwrite the same memory slots; and a storage-layout warning bug that could reverse the AST's linearizedBaseContracts annotation when a contract's variables sit very close to the end of storage, corrupting constructor/state-variable initialization order. The release also adds experimental stack-to-memory spilling to the new SSA-form CFG code generator (behind --experimental --via-ssa-cfg) and removes the now-obsolete experimental EOF backend after EOF's rejection from the Fusaka upgrade.

Solidity

Unsound Spill In Mutual Recursion BugInheritance Order Reversal On Storage End Warning Bug

Blockchain· Added 29 Sep 2026, published 9 Jul 2026

soliditylang.orgadvisory🛡️ Security

Inheritance Order Reversal On Storage End Warning Bug

The Solidity team discloses a medium-severity compiler bug present from Solidity 0.8.29 through 0.8.35 (fixed in 0.8.36): the code that emits Warning 3495 (custom storage layout too close to storage end) uses ranges-v3's actions::reverse instead of views::reverse, permanently mutating the linearizedBaseContracts AST annotation instead of only reading a reversed copy. Because that annotation drives inheritance-order decisions throughout analysis and codegen, affected contracts can get reversed constructor execution order, wrong super/virtual-function and virtual-modifier resolution, and incorrect fallback/receive dispatch — either silently miscompiling or triggering internal compiler errors. Exploitation requires a contract using both inheritance and a `layout at` clause landing within the last 2^64 storage slots, a pattern the team found no real-world evidence of via GitHub and Sourcify scans.

Solidity

Warning 3495ranges-v3actions::reverse+4

Blockchain· Added 29 Sep 2026, published 9 Jul 2026