117 results for “NTIA”

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arxiv.orgpaper🛡️ Security

TextSeal2: Distortion-Free LLM Watermarking with Copula Mixing and Memoryless Races

TextSeal2 is a distortion-free LLM text watermark that merges two or more keys into one virtual key and couples it to fresh noise through AutoMark's Gaussian copula. Under speculative decoding it verifies keyed drafts with an extra exponential race, so output matches the target model and acceptance rate is unchanged. On three LLMs at a 10^-3 false-positive rate, it detects 8 to 22 points more 100-token answers than TextSeal.

TextSeal2TextSealAutoMark+2

Gumbel-max schemespeculative decodingGaussian copula+1

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

arxiv.orgpaper🧠 AI

TextSeal2: Distortion-Free LLM Watermarking with Copula Mixing and Memoryless Races

TextSeal2 is a distortion-free watermark for LLM output that keeps its signal under speculative decoding. It merges two or more keys into one virtual key coupled to fresh noise through AutoMark's Gaussian copula, and verifies drafts with an extra exponential race. On three LLMs it detects 8 to 22 points more 100-token answers than TextSeal at a 10^-3 false-positive rate.

LLMspeculative decodingGumbel-max watermarking+1

TextSeal2TextSealAutoMark+2

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

Aligning Medical Vision Language Models at the Capability Boundary with Entropy Adaptive Rubric Grounding

MedCBR is a two-stage alignment framework for medical vision language models. Difficulty Weighted Boundary Preference Optimization (DBPO) trains on counterfactual preference pairs at the model's capability frontier. Entropy Gated Grounded Policy Optimization (EGPO) then applies distilled clinical rubrics according to prediction entropy. It reaches 61.2% average accuracy on six medical benchmarks, with the largest gains on differential diagnosis, and the two stages combine super-additively.

vision-language modelpreference optimizationreinforcement learning

MedCBRDifficulty Weighted Boundary Preference OptimizationEntropy Gated Grounded Policy Optimization+1

research, training, multimodal· Added 31 Oct 2026, published 29 Oct 2026

arxiv.orgpaper🧠 AI

When LLM Architectures Meet Smart Contract Vulnerability Detection: a Replication Study

This paper replicates Chen et al.'s study of LLM-based smart contract vulnerability detection, first on GPT-4 and then on Mixture-of-Experts, RNN-like and State Space Models. The exact replication confirms the high-recall, low-precision pattern on GPT-4. The differentiated replication finds the pattern does not transfer uniformly: GPT-4 and Gemma-3 behave alike despite a roughly 100x parameter gap, while two MoE models diverge.

LLMTransformerMixture of Experts+3

Chen et al.GPT-4Gemma 3+1

research, evals· Added 30 Oct 2026, published 28 Oct 2026

arxiv.orgpaper🛡️ Security

Session-Bound Behavioral Watermarking for Autonomous LLM Agents

AgentAnchor is a session-keyed behavioral watermark for attributing LLM agent trajectory logs to an owner. It derives per-step keys from a secret key, a fresh public session ID and semantic observation states, and embeds parity evidence through a KL-bounded exponential tilt. The paper formalizes learned cross-session forgery, and a trusted verifier checks disputed logs using wrong-key calibration and abstention.

LLM

AgentAnchorlearned forgery

AI Security· Added 30 Oct 2026, published 28 Oct 2026

arxiv.orgpaper🧠 AI

Session-Bound Behavioral Watermarking for Autonomous LLM Agents

AgentAnchor is a session-keyed behavioral watermark that lets an agent owner prove ownership of a trajectory log. Each step key comes from a secret key, a fresh public session identifier and semantic states of recent observations. Parity evidence is embedded through a KL-bounded exponential tilt of action choices. A trusted verifier checks disputed logs without rerunning the agent, using wrong-key calibration and abstention. The paper also formulates a learned cross-session forgery threat.

LLM

AgentAnchorarXiv

research, agents· Added 30 Oct 2026, published 28 Oct 2026

arxiv.orgpaper🧠 AI

Bayes-Adaptive MDPs for Rollout Allocation to Enhance GRPO Training

AdaRoll-GRPO is a method that allocates GRPO rollout budgets per question by difficulty instead of uniformly. It frames allocation as a sequential decision problem and learns an allocation policy with a Bayes-adaptive MDP, computed offline so training adds no online optimization cost. Experiments on several reasoning datasets and LLM architectures show gains over standard GRPO at the same total rollout budget.

GRPOLLM

AdaRoll-GRPOarXiv

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

arxiv.orgpaper🧠 AI

DCKV: Learned Transform Coding of the Prompt KV Cache in Hybrid Language Models

DCKV is a differentiable codec for the prompt KV cache of hybrid language models, which keep a KV cache only in their few full-attention layers. Transform, quantizer and entropy models (conditioned on prompt tokens and already decoded neighbouring positions) are trained end to end against a frozen model. On Qwen3.5-2B it stores a token in over 55 times fewer bytes than bf16 with at most 1.8 points lost on three long-context benchmarks, and over 100 times fewer with at most 3.6 points lost.

Qwen3.5TransformerLLM

DCKVarXiv

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

eprint.iacr.orgpaper🛡️ Security

Analyzing Compiler-induced Constant-time Violations with Compiler at Scale

CTGREP is a compiler-based framework that applies differential analysis across the whole Clang/LLVM compilation chain to find where, how and under which configurations compiler optimizations break constant-time cryptographic code. Applied to seven libraries, three Clang versions, six targets and thousands of configurations, it led to 48 cases fixed with developers. It also identified seven new problematic LLVM passes and bypassed existing compiler-flag mitigations.

LLVMClang

constant-time violationstiming side-channeldifferential analysis+1

Hardware & RF, AppSec· Added 27 Oct 2026, published 26 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Subexponential Algorithms for LWE via Rejection Sampling on Superlattices

Subexponential Algorithms for LWE via Rejection Sampling on Superlattices is an ePrint paper by Ligeng Fan, Changpeng Shao, Hongbo Li and Dingkang Wang. It extends the Ducas, Engelberts and Loyer superlattice framework to give a provable classical search-LWE algorithm for prime q in [n^2, 2n^2], using m=4n samples and time and space exp(O(n/ln ln n)). A second variant runs in 2^((1/2+o(1))n/ln R) for general parameters. It resolves a gap left by Kirchner and Fouque.

Ducas, Engelberts and LoyerKirchner and FouqueBKW algorithm+1

other· Published and added 26 Oct 2026

arxiv.orgpaper🧠 AI

Continual Learning Exam: Benchmarking Long-Horizon RSI and Experiential Learning in LLM Agents

Continual Learning Exam (CLE) is a benchmark of 120 human-authored tasks, each a five-level ladder (600 levels) across 89 professional roles and 12 occupational domains, built by 50+ domain experts. Agents must pass a level to unlock the next, and an evaluation agent scores submissions against human rubrics. Across 17 harness-model configurations, feedback plus one revision adds a median 5.3 points; the best system clears 47.7% of levels and finishes all five levels in 23.3% of tasks.

LLM

Continual Learning ExamarXiv

evals, agents, research· Added 27 Oct 2026, published 25 Oct 2026

eprint.iacr.orgpaper🛡️ Security

VeilDNS: Post-Quantum DNS Privacy from the First UDP Packet

VeilDNS is a DNS-native protocol that uses a post-quantum KEM and AEAD to encrypt DNS questions and answers and authenticate the resolver in one UDP exchange on port 53, preserving the DNS wire format. It has stateless and stateful modes plus optional forward secrecy from the second query. Against ML-DSA-authenticated DoT/DoH/DoQ it reports up to 3.5x higher cold-session throughput and 13.7x fewer bytes; Tamarin proves query confidentiality, response authentication and forward secrecy.

DNSML-DSATLS+1

VeilDNSTamarinDoT+3

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

eprint.iacr.orgpaper🛡️ Security

Faster and Smaller Anonymous Credentials from Standardized Signatures

GordieHowe is a language, certifying compiler and zero-knowledge proof system for building anonymous credentials on standardized signatures (ECDSA, RSA, ML-DSA). It proves an ECDSA-signed mobile driving license presentation in 64.8 ms with a 276 KiB proof, 10.1x faster than Google's Longfellow, and an ML-DSA-44 signature in 87.3 ms with at least 116 bits of quantum security. Compiler output and security theorems are checked in Lean. It introduces series-parallel IOPs and extends the BCS transformation in the QROM.

ECDSAML-DSARSA+1

GordieHoweLongfellowBCS transformation+3

Identity & Access· Added 24 Oct 2026, published 23 Oct 2026

arxiv.orgpaper🧠 AI

DRIVE: Fine-tuning via Data Contribution- and Diversity-aware Weighting with Prior Regularization

DRIVE is a per-sample weighting framework for single-stage fine-tuning when the source pre-training data is unavailable. It uses one differentiable objective: Shapley additivity splits each sample's contribution into anti-forgetting and adaptation parts, a covariance-based diversity term reshapes them, and a sample-characteristic prior stabilizes them. The authors prove better stability-plasticity guarantees than existing weighting schemes and report better performance, faster convergence and more robustness.

DRIVEarXiv

training, research· Added 26 Oct 2026, published 23 Oct 2026

arxiv.orgpaper🧠 AI

How Much Do You Trust A Dog? Quantifying Hint-Following in Reasoning Language Models

This paper measures how hints such as "[Source] thinks the answer is (X)" shift LLM answers on MMLU questions, varying the source between a professor, a friend and a dog across Qwen, Gemma and SmolLM models. The shift fits an exponential tilt: a roughly constant mean change in the hinted answer's log-odds, with susceptibility largely shared across sources. The shift persists when the source is declared inaccurate or the prompt warns against trusting it. Synthetic experiments show how KL-regularized reward training could produce this pattern.

LLMQwenGemma+1

MMLUarXiv

research, ai-safety, evals· Added 26 Oct 2026, published 23 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Getting to the right root: radical isogenies of every degree

Thomas Decru's ePrint 2026/2595 proves a corrected version of radical isogeny formulae for every N≥4, odd and even, over fields of characteristic coprime to 2N. It settles the sign choice for even N so that 2-isogeny walks stay on the volcano surface. One radical step costs about 12.5N multiplications and one exponentiation, and the paper shows how to evaluate radical isogenies on points, so a CSIDH class group action needs no torsion point sampling.

CSIDH

radical isogeniesKronecker theta function2-isogeny volcano+3

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

Monitorability Is Trainable: Post-Training for Chain-of-Thought Monitorability

Monitorability Is Trainable proposes an unsupervised post-training objective that maximizes mutual information between a model's chain of thought and its output. On three model organisms with degraded CoT (from compression, monitor optimization, and synthetic-document generalization), it recovers most or all lost monitorability. On undegraded policies it improves CoT monitor and activation probe prediction of unsafe behavior and raises consequential reasoning.

LLM

arXiv

ai-safety, research, training· Added 23 Oct 2026, published 21 Oct 2026

arxiv.orgpaper🧠 AI

Symmetry-Based Parameter Efficient Low-Rank Fine-Tuning for High-Throughput Calculation of Lattice Thermal Conductivity

SBFS-FT (symmetry-based few-shot fine-tuning) adapts the MatterSim 1M universal machine-learned interatomic potential with low-rank adaptation on a small set of compounds sharing one crystal symmetry. Tested on half-Heusler (space group 216), space group 225 and spinel (227) datasets, it cut lattice thermal conductivity error 2-3 times versus the base model. It matched per-compound fine-tuning while taking 5-7 times less compute for 100 compounds.

MatterSimLoRA

MatterSim 1Mmoment tensor potentialspolyMLPs

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

eprint.iacr.orgpaper🛡️ Security

Modeling Modular Multiplication for Monomial Prediction and Impossible Differentials, with Application to MSX

This ePrint paper gives the first exact bit-level models of modular multiplication z=(x·y) mod 2^n. One model is for monomial prediction under 3SDP-woU, the other for impossible differential search. Applied to the MSX lightweight cipher, it yields a 7-round integral distinguisher (2^96 data) for MSX-128, key recoveries on 8 and 9 of 18 rounds at about 2^228 operations, and 1,984 seven-round impossible differentials. Both models are implemented in CLAASP.

MSXCLAASP

MSX-128monomial predictionthree-subset division property+5

other· Added 24 Oct 2026, published 21 Oct 2026

eprint.iacr.orgpaper🛡️ Security

A Spectral Theory of Local-to-Global Leakage

A Spectral Theory of Local-to-Global Leakage is an IACR ePrint paper (2026/2571) by Rei Ueno on additive secret sharing over finite abelian groups. It gives an exact leakage decomposition into local leakage magnitude and spectral alignment across shares. Two channels with identical local mutual information can have exponentially different global leakage. Automorphism-weighted sharing can cancel leakage perfectly with finitely many shares, which ordinary additive sharing cannot do.

Rei UenoMöbius inversion

Hardware & RF· Added 21 Oct 2026, published 20 Oct 2026

arxiv.orgpaper🧠 AI

TokenGate: Adapting LLMs for Fast and Memory-Efficient Prefill with Learned Token Pruning

TokenGate is a small drop-in component that adds end-to-end learnable token pruning to trained LLMs for long-context prefill. It uses token-gated attention, inspired by Mixture-of-Experts, to re-weight attention logits with token importance scores, which gets gradients through the non-differentiable top-K step. The authors report 1.4-11.3x faster time-to-first token and 1.5-3.4x lower peak memory with no accuracy loss, on Qwen3 and Qwen3.5.

TransformerQwen3Qwen3.5+2

TokenGatearXiv

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

arxiv.orgpaper🧠 AI

Which Model for Which Role? Cost-Efficient Pareto Recommendation for LLM Agentic Workflows

Cost-Coupled Gittins (CC-Gittins) is a multi-objective extension of cost-aware Gittins search that recommends Pareto-optimal model-to-role assignments for LLM agentic workflows. It exploits the coupling between evaluation and deployment cost, alternating between seeking higher quality and lower cost. On eight quality-cost matrices (two to five roles, up to 1,681 assignments), it recovers a substantial share of the true Pareto frontier at about 10% of exhaustive-evaluation cost, and most of it by 30%. Latency is tested as a third objective.

LLM

arXivCC-Gittins

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

arxiv.orgpaper🛡️ Security

SpanGuard: Decoupling Instruction Localization from Authorization for Lightweight IPI Defense

SpanGuard is an indirect prompt injection (IPI) guardrail that splits detection into two steps. Lightweight unsupervised segmentation first locates candidate instruction blocks in tool outputs, then a small LLM judges whether each block is authorized by the user's request. The authors report a 0.32% attack success rate with negligible benign utility loss, and the guardrail still works with substantially smaller models.

LLM

indirect prompt injectionprompt injection

AI Security· Added 21 Oct 2026, published 19 Oct 2026

arxiv.orgpaper🧠 AI

AI-Driven Reproducibility and Analytical Robustness in Scientific Discovery

SEER-Repro is a benchmark of 20 published papers based on the SEER cancer registry, with the data needed to reproduce their findings. An LLM agent reproduces 77.4% of 2,190 atomic claims. A second agentic pipeline finds alternative analytical choices and reruns the analyses, flagging potentially consequential choices in 9.5% of fully reproduced findings for expert review.

LLM

SEER-ReproSEERSurveillance, Epidemiology, and End Results+1

research, evals, agents· Added 20 Oct 2026, published 16 Oct 2026

arxiv.orgpaper🧠 AI

Acceptance Is Not Speed: Length Inflation in Training-Free Lossy Speculative Decoding

This paper shows that training-free relaxed (lossy) verification in speculative decoding causes length inflation: completions get longer, mostly through extended reasoning traces, which offsets gains in accepted tokens per round. Across twelve target-drafter pairs, relaxed rules accept 1.34x as many tokens per round but produce 1.54x longer completions. Rules that cut verifier rounds usually lose substantial accuracy, so the authors argue completion length and verifier rounds must be reported.

LLM

arXiv

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

arxiv.orgpaper🧠 AI

DLite: History-free Speculative Decoding for Agentic AI

DLite is a history-free speculative decoding framework for agentic LLM workloads. It reuses the target model's contextual hidden states and predicts locally without keeping the draft model's historical KV cache, which avoids the up to 80% acceptance-length drop seen with long, multi-turn contexts. A lightweight sequential head restores intra-block causal dependencies, and a two-stage training strategy is used. It reaches 2-6 acceptance length and beats DSpark by up to 70%.

LLM

DLiteDSparkarXiv

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

arxiv.orgpaper🧠 AI

FedRAPP: Federated Test-Time Prompt Tuning for Vision-Language Models with Reliability-Aware Prompt Personalization

FedRAPP is a personalized aggregation framework for Source-Free Federated Test-Time Prompt Tuning (SF-FTTPT), where clients adapt CLIP soft prompts on unlabeled, non-IID test streams without source data. It combines reliability-aware client assessment, a server-side exponential moving-average prompt memory and similarity-guided peer aggregation. Across nine CLIP benchmark datasets and ImageNet variants it beats standard federated aggregation baselines while keeping prompt-only efficiency.

CLIPvision-language model

FedRAPPImageNetarXiv

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

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