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arxiv.orgpaper🧠 AI

The Athlete as a Scientist of One: Agentic Bayesian Experimental Design for Individualized Physiology from Consumer Wearables

EpiCoach is an agentic system that treats one cross-country mountain biker as an N-of-1 experiment. Claims live in an evidence-gated hypothesis registry, and each must make a probabilistic prediction before a session. Predictions come from a digital twin fitted to heart rate, GPS and altitude. In a replay over eighteen months the twin beat standard forecasters on heart rate but not segment time, and the agent loop matched a static model. In the first three live weeks, the ledger sealed 20 predictions and scored 14, and a safety officer vetoed the agent twice. The registry, benchmark and protocol are released.

LLMBayesian experimental designdigital twin+1
EpiCoacharXiv

agents, research, evals· Added 31 Oct 2026, published 29 Oct 2026

arxiv.orgpaper🧠 AI

Know, then Draw: A New Learning Paradigm for Visual Generation

Know, then Draw is a learning paradigm for visual generation that uses autoregressive pretraining to acquire semantic knowledge and diffusion post-training for high-fidelity rendering. Controlled comparisons find autoregression the stronger pretraining objective, and semantic quality matters more than AR-only generation quality. This motivates a purely semantic tokenizer trained with language supervision only. A 3B text-to-image model trained in 6,600 H100 GPU-hours scores 86 on GenEval and 87 on DPG-Bench.

diffusion modelTransformer
arXivGenEvalDPG-Bench+1

research, multimodal, training· Added 28 Oct 2026, published 27 Oct 2026

arxiv.orgpaper🧠 AI

p-DCIM: A 28nm Probabilistic SRAM DCIM based Ising Machine for Near-Memory Context Selection in LLM-RAG Pipeline

p-DCIM is a 28 nm SRAM digital compute-in-memory macro (0.16 mm2, 0.41 mW) that works as an Ising machine for RAG evidence selection. Selection is cast as a QUBO problem, and the same MAC datapath computes embedding similarities and annealing steps. Replacing LLM reranking with it gives a selection-energy estimate over six orders of magnitude lower and cuts total energy per query by 67%, with comparable token-level F1 on the evaluated examples.

RAGLLMSRAM+2
arXiv

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

eprint.iacr.orgpaper🛡️ Security

OblivDGL: Oblivious Deep Graph Learning Made Practical

OblivDGL is a secure computation framework for GNN training and inference, modelled on Deep Graph Library. It uses an oblivious message-passing paradigm and an oblivious graph representation built on function secret sharing (FSS). It leaks only total node and edge counts. On four benchmark graph datasets it cuts training communication up to 1024x and runtime up to 14.3x against prior secure GNN systems.

Deep Graph LibraryGraph Neural Network
NDSS 2027

AI Security· Added 28 Oct 2026, published 27 Oct 2026

eprint.iacr.orgpaper🧠 AI

OblivDGL: Oblivious Deep Graph Learning Made Practical

OblivDGL is a framework for secure GNN training and inference in machine-learning-as-a-service settings, modelled on Deep Graph Library. It uses an oblivious message passing paradigm and a new oblivious graph representation built on function secret sharing, revealing only the total node and edge counts. On four benchmark graphs it cuts training communication up to 1024x with up to 14.3x speedup, and inference communication up to 754x with 4.19x speedup.

Deep Graph Librarygraph neural networkfunction secret sharing
OblivDGLNDSS

research, training, inference· Added 28 Oct 2026, published 27 Oct 2026

eprint.iacr.orgpaper🛡️ Security

A Kelsey–Kohno Attack Variant against SHA-2 Message Digests in HSS/LMS and XMSS

Falko Strenzke's ePrint 2026/2635 presents a multi-target second-preimage attack on message hashing in HSS/LMS and XMSS with SHA-256 and SHA-256/192. It herds chaining states from many collected signatures into one state, defeating the key and leaf-index prefix. With 2^64 signatures, SHA-256 cost falls from 2^256 to about 2^166 (2^156 at maximal message length). SHAKE256 parameter sets are unaffected.

HSS/LMSXMSSSHA-256+2
Kelsey–Kohno herding attackKelsey–Schneier expandable messagemulti-target second-preimage attack+2

other· Added 26 Oct 2026, published 25 Oct 2026

eprint.iacr.orgpaper🛡️ Security

The Exact Bit Security of Hash-Based Signatures

The Exact Bit Security of Hash-Based Signatures is a 2026 IACR ePrint paper that analyses XMSS with 128-bit digests and SLH-DSA-128s in the random oracle model, with proofs formalised in Lean4. Strong unforgeability comes to 127 bits under most accounting modes but only 122 bits if just direct random oracle queries are counted. The authors give matching attacks, and show how resource accounting changes the result.

XMSSSLH-DSALean
Mikhail KudinovBenedikt WagnerEmile Wambsgans

other· Added 26 Oct 2026, published 24 Oct 2026

eprint.iacr.orgpaper🛡️ Security

When Elliptic-Curve Semiprimality Certificates Reveal Factors

The paper formalizes elliptic-curve semiprimality certificates by adapting the Goldwasser-Kilian primality criterion. For certificates built with complex multiplication, a norm-lattice reconstruction factors N from (N, S, D) alone in expected polynomial time. The author uses it, plus public-point and quadratic-twist attacks, to factor Reble's 1084-digit and Broadhurst's 5061-digit moduli, and shows that hidden-cofactor, withheld-CM-map and private-isogeny repairs also fail.

Goldwasser-Kilian primality criterionReble modulusBroadhurst modulus+4

other· Added 26 Oct 2026, published 24 Oct 2026

arxiv.orgpaper🧠 AI

AI Alignment for Digital Democracy: Fairer Voters Representation against LLM Biases

This paper studies demographic bias in LLM-emulated electorates, using three U.S. presidential elections and comparing one closed-source and one open-source LLM. Removing gender from the voter profile cuts the largest group error by 22-42%, and removing education cuts it by 64%. Removing self-reported race raises mean group error by over 40%. Repeated queries and fairness instructions do not reduce errors and can double them.

LLM
arXiv

research, ai-safety, evals· Added 27 Oct 2026, published 23 Oct 2026

arxiv.orgpaper🧠 AI

TRIAD: Benchmarking Omni-Modal Ambiguity in Multimodal Large Language Models

TRIAD is a benchmark for omni-modal ambiguity resolution. It has 327 hand-written text-image-audio queries in 86 scene groups across 18 ambiguity categories, and removing any one modality makes the answer non-unique. Across 14 omni-modal systems, the best model trails humans by over 20 points in group accuracy. Without answer options, model group accuracy falls to single digits, and leave-one-out tests show audio cues are often underused.

multimodal large language modelomni-modal LLM
TRIADarXiv

evals, multimodal, research· Added 23 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🛡️ Security

Prompt Injection Through Forensic Evidence: A Taxonomy and Proof of Concept of Anti-Forensic Attacks on LLM-Assisted Digital Investigations

Paper on prompt injection delivered through forensic evidence, where the seized device's contents are attacker-controlled and the examiner must read them. It extends the anti-forensic model to cover manipulation of the reasoning layer and gives a taxonomy (objective, technique, payload location, concealment). Three proof-of-concept attacks on an LLM-assisted analysis pipeline change a reported conclusion, write attacker-chosen content to the analyst workstation, and trigger outbound network traffic. Mitigations are proposed.

LLM
indirect prompt injectionprompt injectionanti-forensics+1

AI Security, Blue Team & Detection· Added 24 Oct 2026, published 21 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Enjoy FAEST on Hardware: Efficient and Novel Hardware Acceleration of Emerging Post-Quantum Digital Signature Scheme FAEST

This ePrint paper (2026/2525, October 2026) by Brendan Funk and Jiafeng Xie presents the first hardware accelerator for FAEST, an MPC-in-the-head post-quantum signature in NIST's Round 3 additional-signature process. It analyses FAEST's compute-heavy operations, builds them into hardware blocks, and adds an L1-EM-fast variant. The authors report at least 98% lower equivalent area-delay product than the recent complete SDitH design at the same security level.

FAESTFPGA
NISTSDitHMPC-in-the-head+2

Hardware & RF· Added 19 Oct 2026, published 18 Oct 2026

arxiv.orgpaper🧠 AI

ConQUESTA: Topology-Aware Post-Training Structural Pruning of Quantum Neural Networks

ConQUESTA is a post-training pruning framework for quantum neural networks that combines task saliency with compiler cost, screens candidate entanglers, and searches whole pruning masks under an accuracy constraint. Each mask is transpiled across several coupling topologies. At 55% pruning on 4-, 6- and 8-qubit QNNs it cuts resources by 61.6% (Breast Cancer), 72.0% (Digits01) and 82.2% (Iris), and fixed-mask recovery raises feasible runs from 16/24 to 22/24.

quantum neural network
ConQUESTAarXivBreast Cancer+2

research· Added 20 Oct 2026, published 16 Oct 2026

arxiv.orgpaper🛡️ Security

Approve What, Exactly? Effect Binding and Trusted Rendering for Agent Step-Up Authorization

Paper arguing that agent human-approval prompts count as a second factor only if agent compromise cannot forge them and they name exactly what is approved. It defines twelve attacks on the approval path (decoy recipients, amount cancellation, card padding, label spoofing, enrollment squatting, threshold splitting) and tests them against AP2, Microsoft's action-bound approval design and MCP elicitation. An audit of ten agent runtimes found no third-party verifiable approval record and post-approval argument changes in four of eight. The reference defense ASU (Agent Step-Up) renders the card from a canonical effect descriptor, has a phone passkey sign its digest, and executes only the stored call. It is proven in Tamarin (53 lemmas) and evaluated on 27,108 scripted trials and 609 AgentDojo pairs. The trusted card cut forged-narration attack success from 0.54 to 0.03 and raised LLM-approver catch rate from 0.30 to 0.98.

Model Context ProtocolMicrosoft
Agent Payments ProtocolAgent Step-UpASU+6

AI Security, Identity & Access· Added 17 Oct 2026, published 15 Oct 2026

ethereum-magicians.orgpaper🛡️ Security

ERC: Quantum Bridge Shield

Draft ERC from Quantova Inc that has Ethereum bridges accept cross-chain message batches only when a committee signs the Merkle root with RFC 8554 LMS hash-based signatures. The guard contract requires a strict majority of distinct unfrozen keys, records used leaves, freezes any key that signs a leaf twice, binds digests to domain tag, chain ID, guard address and epoch, and enforces a 60-second delay. It reports gas benchmarks (about 205k per LMS W4 height-10 verify, 12.44M worst-case rotation under EIP-7825), a hybrid LMS+ECDSA migration mode, and a NioLabs assessment with no critical or high findings. It argues that Shor's algorithm makes secp256k1 and BLS committee keys a quantum theft target.

EthereumLayerZero
Quantova IncNioLabsRFC 8554+7

Blockchain· Published and added 14 Oct 2026

arxiv.orgpaper🧠 AI

LLM Agents for Automated Post-Training via Structured Recipe Optimization

Introduces Structured Recipe Optimization (SRO), a paradigm for automated LLM post-training that optimizes over a task-adapted recipe space covering data sources, data processing, cross-domain composition and training configuration. It pairs an LLM-based Recipe-Space Designer, which builds the recipe space for the target tasks, with a Multi-Domain Recipe Optimizer that uses structured trial evidence to guide LLM candidate construction and multi-objective Bayesian trial selection. SRO gets the highest average score in all evaluated target-model and agent-LLM combinations; for example, 41.1 vs 15.4 for the strongest baseline on Qwen3-1.7B with GPT-5.4, and 53.8 vs 40.8 on Qwen3-4B with DeepSeek-V4-Flash, in joint post-training across three capability domains and six benchmarks.

Qwen3LLM
Structured Recipe OptimizationGPT-5.4DeepSeek-V4-Flash+1

research, training, agents· Added 14 Oct 2026, published 13 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Censorship Resistance in Credential Systems and Application to the EUDI Wallet

ePrint paper that defines a security model for censorship resistance in anonymous credential systems, where a malicious issuer uses information gathered at issuance to selectively deny credentials. It gives a round-optimal construction built on blind signatures with selective disclosure at presentation. It also presents CRACS, an instantiation for the European Digital Identity Wallet (EUDIW). CRACS keeps selective disclosure and adds unlinkability between issuance and presentation. It confines the advanced cryptography to issuance, so secure elements, credential formats, signature schemes and verifiers stay unchanged and deployment can be incremental.

EUDI Wallet
CRACSEUDIW

Identity & Access· Added 14 Oct 2026, published 10 Oct 2026

arxiv.orgpaper🧠 AI

From Digital Human Interactions to Physics-Based Humanoid Skills: Physics-Grounded Post-Training of Interaction Generators

The paper introduces DIGHT, a co-adaptive framework that couples a text-conditioned digital-human interaction generator with a physics-based humanoid tracking policy to produce physically plausible two-humanoid interactions. Rather than collapsing executability and interaction-fidelity signals into one scalar reward, it applies physics-decoupled diffusion Direct Preference Optimization (DPO) to align the generator using criterion-specific preferences derived from tracking error, friction, floating, and simulator force feedback (contact occurrence, location, duration, magnitude), then uses the aligned generator's outputs to fine-tune the tracker. Experiments show improved physical plausibility and more reliable, faithful simulated humanoid interactions compared to prior tracking-based approaches.

diffusion modelDirect Preference Optimization
DIGHT

research, training· Added 8 Oct 2026, published 7 Oct 2026

arxiv.orgpaper🧠 AI

Fault-tolerant foundation models

This arXiv paper shows that large language models can be trained to tolerate unreliable, faulty digital hardware, and that their error resilience improves rather than degrades as model size increases. Using modified neural scaling laws derived from 40,000 GPU-hours of training runs on simulated faulty hardware, the authors argue that LLMs learn to compute within "good" error-correcting codes whose relative overhead stays finite regardless of scale, suggesting a path toward running AI inference on low-energy, unreliable hardware for substantial energy savings.

research, training· Added 8 Oct 2026, published 7 Oct 2026

eprint.iacr.orgpaper🛡️ Security

PoP! Goes the VOLE: Shorter Proofs of Possession for KEM Certificates

This IACR ePrint paper proposes and benchmarks a non-interactive Proof of Possession (PoP) for the code-based post-quantum KEM HQC, built using the VOLE-in-the-Head (VOLEitH) zero-knowledge paradigm, to solve the practical problem of issuing KEM-based certificates needed by protocols like KEMTLS. The authors integrate their HQC PoP into the ACME certificate-issuance protocol and compare it against existing PoP schemes for Kyber and FrodoKEM, showing Kyber issues certificates faster but at the cost of protocol or NIST-standard compatibility, while their HQC-based PoP keeps compatibility and achieves smaller proof sizes, which matters under unstable-network conditions due to larger KEM-based CSRs.

ACMEHQCKyber+2

Identity & Access· Added 8 Oct 2026, published 6 Oct 2026

arxiv.orgpaper🛡️ Security

No-Box Vulnerability Analysis: Description-only Detection of Indirect Prompt Injection Vulnerabilities in MCP Servers

This paper proposes no-box vulnerability analysis, a new paradigm for auditing systems using only functionality metadata when neither system access nor runtime interaction is available. The authors build MCPSEC, a prototype that audits Model Context Protocol servers for indirect prompt injection vulnerabilities purely from tool metadata exposed at server registration, evaluating it against 20 widely-deployed MCP servers with 177 tools where human evaluators confirmed 95 vulnerable tools; MCPSEC achieved 98.9% recall versus 84.2% for an LLM baseline, also producing hypothesized exploitation techniques per finding.

Model Context ProtocolLLM
MCPSEC

AI Security· Added 26 Sep 2026

arxiv.orgpaper🛡️ Security

ShellGames: Speculative LLM-Driven SSH Deception

ShellGames is an LLM-driven SSH shell simulator built for cyber deception and moving target defense, addressing prior LLM-honeypot weaknesses (no persistent state, hallucinated output, latency, subversion by curious attackers) via automatic chain-of-thought prompting, memory management for state coherency, speculative command execution, smart routing of complex commands to a sandbox, and subversion detection. The authors introduce a standardized benchmark spanning correctness, consistency, state tracking and robustness, reporting 0.898 command accuracy, 0.918 sequence-level consistency, 0.98 state-tracking accuracy and 0.95 robustness accuracy, all beating baselines by double-digit percentage points, and a 20-participant user study showing realism comparable to a real shell and better perceived coverage than traditional honeypots.

SSHLLM
chain-of-thoughtspeculative command executionsubversion detection+1

Blue Team & Detection, AI Security· Added 26 Sep 2026

arxiv.orgpaper🧠 AI

Quantum Feature Selection for Biomedical Data Analysis

This paper formulates metabolomic feature selection as a Quadratic Unconstrained Binary Optimization (QUBO) problem, balancing feature relevance against redundancy to prepare high-dimensional data for machine learning models like computational biomarkers. The authors test Bias-Field Digitized Counterdiabatic Quantum Optimization (BF-DCQO) and QAOA on gate-based quantum hardware, comparing against classical filter, wrapper, and embedded feature selection methods (including exhaustive search and Iterative Tabu Search) on three Autism Spectrum Disorder metabolomic datasets. Results show reduced runtime versus exhaustive/Tabu search and competitive classifier performance, though the authors explicitly do not claim quantum advantage, framing the work as a NISQ hardware feasibility demonstration.

Autism Spectrum Disorder

research· Added 26 Sep 2026, published 24 Sep 2026

arxiv.orgpaper🧠 AI

MintAct: A Unified Visual Agent for Digital Environments

MintAct is a family of vision-language models (2B/4B/8B) that unifies UI grounding, multi-step navigation across mobile, desktop, and web, and visual tool use into a single agent, matching per-domain specialist performance. The authors built a scalable environment and asynchronous RL infrastructure that hosts hundreds of concurrent heterogeneous backends for trajectory collection and online RL while staying stable under noisy feedback and off-policy drift, achieving 48.9 on OSWorld-Verified at competitive model sizes.

vision-language model
MintActOSWorld-Verified

multimodal, agents, research, training· Added 22 Sep 2026, published 18 Sep 2026

arxiv.orgpaper🛡️ Security

AI Agents May Always Fall for Prompt Injections (Abdelnabi & Bagdasarian)

This arXiv paper by Abdelnabi and Bagdasarian argues that the dominant prompt-injection defense paradigm — data/instruction separation — fails to catch attacks built on contextual manipulation and also breaks legitimate agent behavior. The authors reframe prompt injection through Contextual Integrity (CI), a privacy theory judging whether information flows comply with contextual norms, and construct scenarios where attackers misrepresent flows, manipulate norms, or mix multiple flows to bypass defenses. They derive an impossibility result: any adversary can construct a context making a blocked flow look legitimate, or any defender who tightens norms enough will block genuinely legitimate flows, and propose CI-aware alignment as a principled path forward for frontier autonomous agents.

LLM
Contextual IntegritySahar AbdelnabiEugene Bagdasarian

AI Security· Added 18 Sep 2026

arxiv.orgpaper🧠 AI

Embedding Models Measure in Peculiar Ways

This paper studies whether embedding models' notions of semantic similarity align with objective physical measurements of mass, distance, time and volume. The authors find that physical measurement is only weakly captured in embedding space, and that embedding similarity for quantities is instead heavily driven by superficial string similarity (e.g., shared digits or units) rather than true magnitude proximity, with recalibration attempts failing to fix the misalignment.

evals, research· Added 22 Sep 2026, published 17 Sep 2026

arxiv.orgpaper🧠 AI

A Zeroth-Order Paradigm for LLM Preference Alignment

Proposes Comparison-based Preference Optimization (ComPO), a zeroth-order alternative to direct preference alignment methods (like DPO) that extracts directional information from preference pairs via comparison oracles rather than optimizing a differentiable preference loss, aimed at mitigating likelihood displacement. The paper establishes convergence guarantees for an offline scheme and a performance guarantee for an online variant using reverse-KL control against a reference policy, and reports experiments on Mistral, Llama, Gemma-2, Qwen3, and Gemma-3 showing improved length-controlled win rates over existing direct alignment baselines.

MistralLlamaGemma-2+2

training, research· Added 22 Sep 2026, published 16 Sep 2026