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arxiv.orgpaper🧠 AI

On the Fundamental Limits of Prefill-Based Decoding Length Prediction

This paper derives bounds on predicting an LLM's decode length from prefill-phase hidden states, a primitive for inference scheduling. It models decoding as a stopped stochastic process, giving per-prompt normality and an affine variance law σ=αμ+b. Predictors (MLP, EGTP, FiLM, soft-label, Mixture-of-Experts) converge near the bound, so system error dominates model error. It proposes fixing model, data and sampling in benchmarks and reporting relative-error metrics.

LLMMixture of ExpertsMLP+2
arXiv

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

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

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

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

arxiv.orgpaper🛡️ Security

Ranking Is Not Deciding: Benchmark Shortcuts and Operating-Point Collapse in Query-Time RAG Poisoning Defenses

Ranking Is Not Deciding is an October 2026 arXiv paper showing that query-time RAG poisoning filters score near-perfectly because of how the poison set was built. PoisonedRAG's code prepends the target question to each passage. Removing that prefix takes RAGuard from AUROC 0.953 to 0.758 and its miss rate from 0.191 to 0.763. The authors release a script that profiles a poison set.

LLM
PoisonedRAGRAGuardTrustRAG+5

AI Security· Added 27 Oct 2026, published 24 Oct 2026

arxiv.orgpaper🧠 AI

An actively trained neural operator for near-explicit atmospheric chemistry

FNO-MoE is a Fourier neural operator with a mixture of experts that predicts the full near-explicit chemical state (Master Chemical Mechanism, thousands of species) over 30 minutes in one call. Expert disagreement selects the most informative box-model simulations, so active sampling beats random or evenly spread sampling at equal cost. Fine-tuning on longer trajectories keeps multi-hour runs stable, and the emulator runs orders of magnitude faster than the numerical solver.

Fourier neural operatorMixture of Experts
Master Chemical MechanismFNO-MoEarXiv

research· Added 27 Oct 2026, published 24 Oct 2026

arxiv.orgpaper🧠 AI

RAQ-MLA: Role-Aware Quantization for Multi-Head Latent Attention Caches

RAQ-MLA (Role-Aware Quantization for Multi-Head Latent Attention Caches) is a training-free method for quantizing the MLA KV cache. It fits a separate invertible transform to the compressed latent (tuned for attention-weighted value error) and to the positional key (tuned for score perturbation). It reaches about 2.5 bits per element, a 6.5x gain in theoretical KV-cache capacity, with near-lossless reasoning and coding results on four MLA models.

Multi-Head Latent AttentionLLMTransformer
arXiv

research, inference· Added 27 Oct 2026, published 24 Oct 2026

arxiv.orgpaper🛡️ Security

On Task, Off Track: Benchmarking LLM and Agent Robustness to Near-Target Prompt Injection

NTBench (Near-Target Benchmark) tests LLM and agent robustness to near-target prompt injection, where injected instructions steer a response toward a plausible but incorrect or harmful outcome instead of abandoning the task. It builds reference-guided target-instruction pairs on existing datasets, with human validation, and evaluates seven LLMs on ten language datasets and two agent benchmarks. Attack success rose 9.3 to 35.8 points (incorrect targets) and 1.7 to 17.3 points (harmful targets) over fixed-target baselines.

LLM
NTBenchnear-target prompt injectionprompt injection

AI Security· Added 26 Oct 2026, published 23 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🛡️ Security

Combining Long-Term Memory and LLMs Comes at a High Cost: From Prompt Injection to Persistent Compromise

Paper on memory injection, where prompt injection persists across sessions through the long-term memory of LLM-as-a-service platforms. It presents a general threat model and shows all NIST attack goals are achievable on ChatGPT Instant 5.3, Claude Sonnet 4.6 and Gemini 3 Flash. Vectors include personal memories, projects, skills, organization instructions and MCP resources. On Claude, shared memory lets an attacker compromise multiple users in an organization and exfiltrate prompts. Nearly half of the derived mitigations are unimplemented.

ChatGPTClaudeGemini+2
memory injectionprompt injectionNIST+2

AI Security· Added 24 Oct 2026, published 20 Oct 2026

arxiv.orgpaper🧠 AI

AgenticRoboRL: Bringing Coding-Agent Post-Training to Agentic Robotics

AgenticRoboRL is a framework for post-training robot planners across different harnesses, combining supervised fine-tuning with multi-turn reinforcement learning. Harness-specific adapters link recorded planner interactions to task outcomes, so one RL pipeline covers both VLA skill invocation and control-code generation. On LIBERO-PRO, a 9B planner rose from 18.1% to 71.0% without external memory, near Codex (GPT-5.5) with memory at 72.1%. Code and checkpoints are promised.

Codexvision-language-action model
AgenticRoboRLLIBERO-PROGPT-5.5+1

research, training, agents· Added 22 Oct 2026, published 20 Oct 2026

arxiv.orgpaper🛡️ Security

Context-Aware Prompt Injection Detection via Small Language Models in RAG Pipelines: nokast-secureRAG

nokast-secureRAG is a roughly 0.5B-parameter small language model that works as a semantic firewall between retrieval and generation in local RAG systems. It classifies each query and context pair as safe, suspicious or malicious. It reaches 0.994 recall at a 0.026 false-positive rate in-distribution, beating Llama Guard 3 1B and Prompt-Guard-86M (0.900 recall, 0.690 FPR). Recall on PoisonedRAG falls to near zero, and single-request latency is 293-404 ms.

Llama Guard 3LLM
indirect prompt injectionretrieval poisoningPoisonedRAG+2

AI Security· Added 22 Oct 2026, published 20 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

arxiv.orgpaper🧠 AI

Assisted Turbulence Modeling via Data Assimilation and Machine Learning for Turbomachinery Flows

This paper presents a hybrid framework for axial compressor flow that couples a Multi-Fidelity Ensemble Kalman Filter (MFEnKF) with machine learning. The filter merges PIV experiments with steady RANS and unsteady hybrid RANS/LES simulations. A neural network trained on the enriched data predicts Navier-Stokes volume forcing terms from velocity, velocity gradients and turbulent viscosity. A Mixture of Experts fixes local errors near blade tips, giving lower experimental discrepancy than baseline RANS.

Mixture of Expertsneural networkMulti-Fidelity Ensemble Kalman Filter+3
MFEnKF

research· Added 21 Oct 2026, published 19 Oct 2026

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