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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

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

arxiv.orgpaper🛡️ Security

SEAL: Sealed Queries for Local Sanitization against Indirect Prompt Injection

SEAL is a defense against indirect prompt injection for local LLM agents. It learns a soft prompt and scoring readout offline, then uses a frozen task model to score and delete injected tokens from tool outputs, with queries built from trusted initial context. On AgentDojo, attack success falls from 34.6% to 0.1% and Joint rises from 51.4% to 77.5%, using about 127x fewer defense tokens than PISanitizer.

LLMAgentDojoAgentDyn+1
indirect prompt injection

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

eprint.iacr.orgpaper🛡️ Security

High Precision Noise Cleaning for CKKS with Application to Asynchronous Threshold CKKS

High Precision Noise Cleaning for CKKS is an IACR ePrint preprint (2026/2675) by Zhihao Li, Cheng Hong and Tao Wei. It composes lower-degree Hermite polynomials to reach output noise bounds such as 2^-300 for noise flooding, and uses this to build an asynchronous threshold CKKS scheme with strong simulation security in the random oracle model. In OpenFHE, binary-message cleaning falls from 9.63 s to 1.54 s.

CKKSOpenFHE
noise floodingHermite interpolationfunctional bootstrapping+2

other· Added 31 Oct 2026, published 29 Oct 2026

arxiv.orgpaper🧠 AI

inkGround: Benchmarking Fine-grained Visual Grounding Capabilities of Vision-Language Models for Cursive Handwriting in the Wild

inkGround is a benchmark for stroke trajectory recovery from full-page, camera-captured handwriting, with 537 page images (about 18.7k words, 163k strokes) and pseudo-label references filtered by OCR agreement and human raters. Models get a page image and a target word, then output ordered stroke points. Seven VLMs were scored with DTW, Chamfer distance, readability and style similarity. Closed-source models, notably Gemini-3.1-Pro, did best, and the ranking held on IAM-OnDB.

vision-language model
inkGroundIAM-OnDBGemini-3.1-Pro+1

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

arxiv.orgpaper🧠 AI

Folding Attention Biases into Keys Makes Compacted KV Caches Run on Standard Kernels

The paper folds the per-key logit bias of Attention Matching (AM) KV cache compaction into the keys along one closed-form direction per head, fitted on context queries. The result is a plain cache that FlashAttention and vLLM paged caches can use, with output error bounded by how far each query's projection on that direction is from 1. Unmodified vLLM serves folded caches through a KV connector. On QuALITY and LongHealth the fold is within noise of AM in 13 of 14 settings. Under AM's published per-head budgets it is within noise in 9 of 10.

vLLMFlashAttentionHugging Face+3
Attention MatchingQuALITYLongHealth+2

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

arxiv.orgpaper🧠 AI

Explaining Navigation Decisions: Stage-Aware Visual Attribution for Vision-Language Navigation

A stage-aware visual attribution framework for Vision-Language Navigation (VLN) that explains a robot's action choices. It identifies "instruction-stage insensitivity", where existing explainers give similar outputs across navigation stages. The method builds action-conditioned temporal instruction-visual attributions, aggregates relevant historical evidence by stage, and applies an instruction-guided spatial filter. It improves attribution quality across several VLN policies and benchmarks, helps diagnose failure cases, and was tested on a Unitree G1 robot.

vision-language model
Unitree G1

research, multimodal· Added 27 Oct 2026, published 25 Oct 2026

arxiv.orgpaper🧠 AI

Eat Your Precision: Precision Objective Mitigates Reward Hacking in Rubric-based RL

FORK is a reward for rubric-based reinforcement learning that jointly optimizes recall and a new precision objective. Precision requires each unit of a response to be necessary (rubric-driven necessity credits) and factually correct (a grader). Rubric-coverage rewards otherwise produce verbose, less accurate responses. FORK keeps similar coverage with shorter outputs, raising win rate against the base policy from 10.6% to 68.0% on RubricHub-Medical and from 22.1% to 74.1% on HealthBench.

LLM
HealthBenchRubricHub-MedicalFORK

training, research· Added 27 Oct 2026, published 24 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

eprint.iacr.orgpaper🛡️ Security

Pairwise Independence of Ten-Round AES

Pairwise Independence of Ten-Round AES (Zhongfeng Niu, Kai Hu, ePrint 2026/2608) proves that ten rounds of AES with independent uniform round keys and input/output whitening are within 3017/2^141 (below 2^-129) statistical distance of pairwise independent. The proof bounds the averaged difference-distribution matrix over five superbox layers, using Gram moments and MDS code constraints. Final-round MixColumns omission is retained.

AES
pairwise independencedifference-distribution matrixMDS code+1

other· Added 24 Oct 2026, published 23 Oct 2026

arxiv.orgpaper🧠 AI

The Geometry of Metaphor: Probing and Steering Metaphor Representations in Large Language Models

The Geometry of Metaphor is an October 2026 arXiv preprint that uses mechanistic interpretability to find causal subspaces for metaphor in LLMs of different architectures and sizes. The authors steer output on classification tasks across parts of speech. They also steer generation tasks, showing the metaphoricity of model output can be controlled. The abstract gives no model names or metrics.

LLM
arXiv

research· Added 24 Oct 2026, published 22 Oct 2026

arxiv.orgpaper🧠 AI

RIFT: KV Cache Compression for Draft Models with Complementary Compressibility Training

RIFT is a family of selection-based KV cache compression strategies for speculative-decoding draft models. It targets "decode staleness", where prefill-focused selection loses relevance as output grows, by balancing prefill information with recent generated tokens, refreshing the selection adaptively, and training drafters with sparse attention masks. Qwen3.5-4B drafting for Qwen3.5-122B keeps over 98% of full-cache acceptance length at 32K input, 4K output and an 8K KV budget.

Qwen3.5TransformerLLM
arXiv

research, inference· Added 23 Oct 2026, published 22 Oct 2026

eprint.iacr.orgpaper🛡️ Security

The Good, the Bad, and the Noisy: A Systematic Analysis of Ring-Oscillator-Based Noise Generators

This IACR ePrint paper (2026/2597) systematically compares ring-oscillator-based additive noise generators, a hiding countermeasure against side-channel attacks. Experiments show many existing designs ignore output amplification and the risk of the noise being filtered out, so they lower signal-to-noise ratio too little. The authors give a guideline on inverter chain length, output amplification and frequency randomization, and build a tailored generator for an AES implementation that outperforms prior designs.

AES
Florian StolzDina HesseJulius Hermelink+2

Hardware & RF· 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

Cache Engineering for Agents

Cache Engineering for Agents proposes letting LLM agents control their active KV cache directly, using a drop operation to exclude cache from attention and repos to adjust retained keys' position encodings, without editing the trajectory or re-prefilling. The authors build the AKV serving engine to handle history-dependent cache states. A tool-response rolling-drop policy raises average benchmark scores by 1.7 points and cuts cost by 27.7% against threshold-triggered summarization. AKV gives up to 1.2x output throughput and 1.5x faster P95 latency.

LLMAKV
arXiv

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

arxiv.orgpaper🧠 AI

Reward Misspecification Degrades Chain-of-Thought Monitorability

This paper shows that reward misspecification alone degrades chain-of-thought (CoT) monitorability in language models. Using a testbed of paired true and misspecified rewards across three models and three tasks, CoTs keep reasoning about task intent while outputs hack the reward. The proposed reward description training injects an LM-inferred reward description into the input and raises monitorability by an average of 24% or 42% absolute, depending on the metric.

LLM
arXiv

ai-safety, training, research· Added 22 Oct 2026, published 20 Oct 2026

arxiv.orgpaper🧠 AI

Same Simulation, Different Evidence: Where Retrieved Tool Output Costs LLM Agents Accuracy

This paper studies how keeping long tool output out of the context window, and reading it through a section index instead, costs LLM agents accuracy. Using the PHREEQC geochemistry simulator on 158 execution-verified multiple-choice items and 11,007 logged calls, GPT-5.1 and GPT-5.2 lose 11.3 and 7.9 points, while Claude Opus 4.6 and Sonnet 4.6 show no detectable cost. The authors name the failure evidence substitution: agents skip the report and misround from their own output files.

PHREEQCLLMClaude+1
USGSGPT-5.1GPT-5.2+4

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

arxiv.orgpaper🧠 AI

EpiLens: Lightweight RAG for Epidemiological Knowledge Acquisition

EpiLens is a lightweight, open-source retrieval-augmented generation package that applies a fixed retrieval template and output schema to every paper in a corpus and links outputs to source passages. On a corpus of 2,297 epidemiology papers, its classifier matches or exceeds supervised and zero-shot baselines on four tasks without task-specific training data, scored against 214 manual labels. It also compares model-based study shares across parameters and pathogens.

RAGLLM
EpiLensSARS-CoV-2mpox+1

llm-apps, research· 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

eprint.iacr.orgpaper🛡️ Security

From if to Oops: Turning a conditional into a key-recovery oracle

The paper presents a fault attack on deterministic CSIDH and OSIDH-LD. Forcing the lane-selection mask to zero (for example by skipping one instruction) makes output changes reveal whether a secret exponent reaches a threshold. Binary search then recovers the full key in at most 297 queries for OSIDH-LD and 222 for deterministic CSIDH with CSIDH-512-sized keys. Full OSIDH-LD keys were recovered in software fault simulation, and a ChipWhisperer-Lite skipped the target instruction with probability at least 0.94. The authors argue fault resistance requires giving up determinism and propose randomization countermeasures.

CSIDHOSIDH-LDChipWhisperer-Lite
deterministic CSIDHCSIDH-512Houben+6

Hardware & RF· Added 20 Oct 2026, published 19 Oct 2026

anthropic.compaper🧠 AI

How employment is changing at Anthropic

"How employment is changing at Anthropic" is an October 2026 technical report by Nathan Wilmers, Regina Catipon, Maxim Massenkoff and Peter McCrory. It uses internal employment, agent telemetry, output, survey, calendar and hiring data plus 17 interviews. Junior technical hiring fell from 27% to 9%, agent hours reached six times employee hours by August 2026, and adopters' output rose. Employees increasingly do meta-work managing agents.

ClaudeClaude CodeSlack+1
AnthropicNathan WilmersRegina Catipon+5

ai-policy, agents· Added 19 Oct 2026, published 18 Oct 2026

huggingface.copaper🧠 AI

Doc2Harness: Benchmarking LLMs on Long-Horizon Harness Engineering of Coding Agents

Doc2Harness is a two-stage benchmark for harness engineering. In Stage 1 a build model, working inside a fixed builder harness, gets an interface-only contract skeleton plus a development document consolidating behavior from twelve frontier harnesses, and builds a full candidate harness in an isolated container. In Stage 2 the frozen harness runs in separate per-test-case containers, driven by a scripted mock model, and is scored on four channels: requests sent to the model, files on disk, exit code and command output. Across 19 frontier models with three builds each on 328 test cases, GPT-6 Astra leads at 53.05% against 27.44% for the weakest, and failures cluster around how cross-cutting functionality is split across modules.

Docker
Doc2HarnessGPT-6 AstraHugging Face

evals, coding-agents, agents· Added 16 Oct 2026

arxiv.orgpaper🧠 AI

DK-VLM: Geometry as Position for Spatial Reasoning in Vision-Language Models

DK-VLM adds Dual-Kernel Attention to a vision-language model. A semantic kernel keeps the pretrained attention and positional encoding. A separate geometric kernel uses estimated 3D points and camera poses to modulate attention through point displacements and relative view transformations, and a learned gate fuses the two outputs. Geometry therefore enters attention as position, not as fused 3D reconstruction features. The paper also adds Reference-Frame Composition (RC) supervision, which gives stepwise turn and heading targets derived from verified single-step annotations, to train multi-step spatial reasoning as the observer moves. DK-VLM-8B reports the highest average score across six spatial benchmarks among the compared methods, with 74.33 on VSI-Bench and 58.28 on ReVSI-32, and it preserves general video understanding.

vision-language modelTransformer
DK-VLMVSI-BenchReVSI-32+1

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

arxiv.orgpaper🧠 AI

DropKV: Decoupling Residual-Output Perturbation for Near-Optimal KV-Cache Eviction

DropKV is a KV-cache eviction framework for long-context LLMs. It decouples the joint eviction decision into independent per-token scores based on residual-output perturbation, which avoids the NP-hard combinatorial problem. It has a provable constant-factor approximation guarantee under a cone condition, which the authors verify empirically on three long-context LLMs. The fused Triton kernels never materialize the full attention matrix. They give a 19.8x scoring-kernel speedup and up to 9.9% end-to-end prefill speedup. On RULER and LongBench it beats state-of-the-art eviction baselines at equal cache budgets.

TritonLLMTransformer
DropKVRULERLongBench+1

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

arxiv.orgpaper🛡️ Security

From Detection to Decision: Calibration and Deferral for Prompt-Injection Detection

Evaluates a native probabilistic decision model (Jev), Llama Prompt Guard 2 (22M and 86M) and an LLM judge on 47,611 held-out examples covering direct/indirect prompt injection, hard-benign inputs, obfuscation and distribution shift. The LLM judge has the best AUROC (0.897), followed by Jev (0.871), Prompt Guard 2 86M (0.757) and 22M (0.714). Jev's native probabilities do not consistently beat post-hoc calibrated classifier scores, and temperature scaling improves calibration but not discrimination. At a 1% false-negative-rate target, every system needs over 91.8% escalation, so probabilistic outputs alone do not give reliable security decisions under distribution shift.

LlamaLLM
JevPrompt Guard 2prompt injection+1

AI Security· Added 17 Oct 2026, published 14 Oct 2026

arxiv.orgpaper🧠 AI

ChunkAct: Action Chunking for Agentic Robotics

ChunkAct brings action chunking to agentic robotics, where an LLM agent composes perception, motion and VLA tools guided by memory. Online, one call launches a chain of primitives that runs autonomously and returns control only on failure. Offline, memory is compiled into these chunks so execution needs no memory at test time and the planner is used only for real decisions. On LIBERO-Pro it improves task success by 5.0 points over the prior agentic-robotics state of the art, with 2.9x fewer LLM calls, 4.4x fewer input tokens and 2.3x fewer output tokens.

LLMvision-language-action model
ChunkActLIBERO-Pro

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

arxiv.orgpaper🧠 AI

EmoGenie: Benchmarking and Evaluating Expressed and Evoked Emotion in T2I Generation with MLLMs

EmoGenie is a benchmark for emotions expressed and evoked by text-to-image outputs. It has 6,400 images from eight T2I models using 800 emotion-targeted prompts. It includes EmoGenie-QA (90,000 closed-set and 1,057 open-ended questions) and EmoGenie-Assess (13,175 mean opinion scores for valence, arousal, dominance and aesthetic quality). The authors also propose EmoProbe, which pairs emotion QA with response-aligned, dimension-specific regression heads. They report that it beats baselines on emotion QA and correlates better with human ratings. The work shows how reliably MLLMs interpret affect in generated images.

multimodal large language modeltext-to-image
EmoGenieEmoGenie-QAEmoGenie-Assess+2

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

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