158 results for “LMS”

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.

arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026🛡️ Security

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.

arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026🧠 AI

PhysCount is a benchmark of 546 real-world images and 4,595 human-written questions in 651 target-specific groups. It tests whether multimodal LLMs can separate visible appearances (direct view, reflections, glass) from the physical entities behind them. Across 40 MLLMs, counting appearances is much easier than linking them to entities, and the best model answers every question correctly in only 13.36% of groups.

arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026🧠 AI

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.

arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026🛡️ Security

A five-task benchmark tests multimodal LLM color judgment against CIE colorimetric ground truth: AWB pair judgment, skin-tone bias direction, CCT estimation, Delta E 2000 pair ordering, and color naming under changing illuminants. Eight endpoints were tested. On ColorChecker-24 stimuli, closed frontier models reach CCT MAE of 715-1056 K versus 1358-2133 K for open-weight models, and a non-ColorChecker control shows this gap is partly template-dependent.

arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026🧠 AI

TrussTutor-Bench is a benchmark of 200 verified two-dimensional truss problems from statics, paired with automatically generated student solutions. It evaluates thirteen LLMs on solution accuracy (64.5% to 97.0%), error detection (90.4% to 100%) and sycophancy. Models rejected their own correct solutions in 1.6% to 50.0% of cases when a student insisted the work was wrong. Sycophancy separated models; error detection did not.

arxiv.orgpaperAdded 31 Oct 2026, published 28 Oct 2026🧠 AI

This paper studies whether vision-language models can infer how long a person stays somewhere (living, staying, or leaving) from geotagged social media posts. The authors build a new corpus of geotagged tweets co-annotated by humans and LLMs. State-of-the-art models struggle even with multi-turn Chain-of-Thought prompting, often missing contextual cues and subtle hashtags while over-relying on prominent visual details.

arxiv.orgpaperAdded 30 Oct 2026, published 28 Oct 2026🧠 AI

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.

arxiv.orgpaperAdded 30 Oct 2026, published 28 Oct 2026🧠 AI

LongChang is an automated framework that builds simulated worlds with altered physical laws, such as heavier objects falling more slowly, to test whether LLMs can find the law by free exploration. Explorer LLMs manipulate objects and build tools without tasks or hints, while separate LLM agents generate scenes, review tool designs and grade reports. Across 12 LLMs, GPT-6 Astra reached a 16.7% discovery rate and the other 11 scored 0%.

arxiv.orgpaperAdded 28 Oct 2026, published 27 Oct 2026🧠 AI

VideoLVR is a method that lets video multimodal LLMs build latent visual evidence from local regions at relevant moments and reason over it interleaved with text tokens. A visual token prediction objective, defined over a vocabulary derived from the input video, supervises the latents. It extends to reinforcement learning with sampled visual tokens as actions and final-answer rewards, and improves fine-grained video benchmarks without much context growth.

arxiv.orgpaperAdded 28 Oct 2026, published 27 Oct 2026🧠 AI

This paper defines a character naming task for films: given a name spoken in dialogue and its video context, the model must pick which visible character bears that name. It provides a dataset with annotations for character naming and first appearances, a video-and-dialogue model, and a comparison with proprietary multimodal LLMs. It also proposes a progressive framework that builds a character bank as the film plays, without cast lists or actor photographs.

arxiv.orgpaperAdded 28 Oct 2026, published 27 Oct 2026🧠 AI

This paper studies how multimodal LLMs refuse questions that an image cannot answer, using the MoHoBench benchmark on three MLLMs. It varies the meaning and placement of refusal instructions, compares supervised and preference-based post-training, and intervenes on a refusal-related activation direction. Instructions change refusal at the sample level in ways aggregate rates hide. Post-training raises refusal most but can mix refusal with attempted answers. Activation intervention gives smaller, transferable changes.

arxiv.orgpaperAdded 28 Oct 2026, published 27 Oct 2026🧠 AI

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.

eprint.iacr.orgpaperAdded 26 Oct 2026, published 25 Oct 2026🛡️ Security

Open bilingual (Russian and UK English) benchmark of five open pseudonymisation tools and a regex baseline, plus a new engine built on the two strongest tools. On a held-out set of 1,000 synthetic documents per language, the best existing tools leave a whole personal value visible in 61% (Russian) and 58% (UK) of documents. The engine does so in 0.3% and 0.2%. Russian name restoration reaches only 94.6%, below the 99% threshold. Real documents were not tested.

arxiv.orgpaperAdded 27 Oct 2026, published 25 Oct 2026🛡️ Security

This paper presents an open benchmark of five existing pseudonymisation tools and a regex baseline on Russian and UK English documents, plus an open engine built on the two strongest tools. On a held-out set of 1,000 synthetic documents per language, the best existing tools leave a whole value visible in 58 to 61% of documents. The engine does so in 0.2 to 0.3%. Russian name restoration reaches only 94.6%, below the 99% threshold. Real documents were not tested.

arxiv.orgpaperAdded 27 Oct 2026, published 25 Oct 2026🧠 AI

This paper presents a pipeline that uses persona-conditioned vision language models to surface interpretive ambiguity in visualizations. Topic-relevant personas elicit candidate takeaways, and a Target-Pattern-Scope framework lets LLM judges label statement pairs as Compatible, Disjoint or Opposing, which are summarized in an ambiguity profile. The authors benchmark VLMs on chart comprehension per chart type, check labeling against human annotations, and run case studies on public visualizations and a redesign.

arxiv.orgpaperAdded 27 Oct 2026, published 25 Oct 2026🧠 AI

InteractiveWorld is an environment for closed-loop interaction between an AI assistant and a simulated human agent who sees the scene, follows instructions and acts independently. InteractiveBench, built on it, has 120 multi-turn tasks in six categories scored by verifiable, state-based rewards. Across 12 frontier MLLMs, the best model, Claude Fable 5.1, reaches at most 20% on long-horizon Guidance and 25% on Planning tasks.

arxiv.orgpaperAdded 27 Oct 2026, published 25 Oct 2026🧠 AI

DRILS is an LLM-based agentic framework for requirements change impact analysis. It uses multiple agents to generate candidate impact sets, with an LLM-as-a-judge promoting diversity in selection. It adds RE-RAG, a tuned retrieval technique, and a requirement dependency graph as a knowledge source. On three curated datasets it reaches 87.1% recall, 76.3% precision and 81% F1 at lower cost than baselines. The authors release the datasets.

arxiv.orgpaperAdded 27 Oct 2026, published 24 Oct 2026🧠 AI

AutoTavern is a self-improving system of specialised LLM research agents with a typed shared memory and verification gates. In 20 days it built TavernGym, a full-content Hearthstone Battlegrounds simulator, and 23 versions of TavernAgent, a PPO-trained player. TavernAgent averaged placement 1.60 with 80% first places over its last 20 live games, beating the top ten players (2.34, 59%). On TavernBench (960 tasks), it answered 91% of play tasks against at most 63% for frontier LLMs.

arxiv.orgpaperAdded 27 Oct 2026, published 24 Oct 2026🧠 AI

TriageRAG is a failure-aware self-training method for adapting retrieval-augmented LLMs to specialized domains using only an unlabeled corpus. It probes the student on self-generated questions with and without retrieved context, sorts them into known, solved, context-failure and retrieval-failure, and routes each to a matching training signal. With Qwen3-4B on 11 datasets it beats budget-matched SimRAG by 1.2-6.4 points and recovers closed-book accuracy where retrieval hurts.

arxiv.orgpaperAdded 27 Oct 2026, published 24 Oct 2026🧠 AI

RALE (Representation-Aware Logical Ensemble) is a method for detecting vulnerabilities in C source code with LLMs trained on different code representations: raw source, Abstract Syntax Trees and Control Flow Graphs. The authors show the representations suit different vulnerability classes, and combine the models with explicit logical rules and majority voting. Single-representation models reach up to 73% F1, while the ensemble reaches 78%.

arxiv.orgpaperAdded 28 Oct 2026, published 24 Oct 2026🛡️ Security

RALE (Representation-Aware Logical Ensemble) is a method for LLM-based vulnerability detection in C code. The authors train LLMs on raw source code, Abstract Syntax Trees and Control Flow Graphs, and find each representation works best for certain vulnerability classes. Combining the models with explicit logical rules and majority voting raises F1 from at most 73% for a single model to 78%, with gains in accuracy, precision and recall.

arxiv.orgpaperAdded 28 Oct 2026, published 24 Oct 2026🧠 AI

VulDec is a dataset and framework for detecting vulnerabilities in binaries by first decompiling them with fine-tuned LLMs. The dataset aligns real-world Assembly-C function pairs across several architectures with CWE labels. Tuned neural decompilers reach about 56% Edit Similarity and 43% BLEU-4, and tuned detectors reach a macro F1 of 0.61 on a realistic test set with a 3% vulnerability rate.

arxiv.orgpaperAdded 28 Oct 2026, published 24 Oct 2026🧠 AI

Provable Security of SHA-256-Based LMS Revisited in the Ideal-Cipher Model is an October 2026 ePrint paper by Chun Guo, Hao Cheng, Bart Preneel and Meiqin Wang. It shows forgery attacks on LMS_SHA256_M32 and LMS_SHA256_M24 that exploit Merkle-Damgård structure, so the Katz and Fluhrer random-oracle bounds do not apply. It proves ideal-cipher-model bounds: about 256 minus log2 of the block count for M32, and a minimum of two terms for M24.

eprint.iacr.orgpaperAdded 26 Oct 2026, published 24 Oct 2026🛡️ Security

The paper builds two corpora, EventTyp and the human-validated EventTypGold, to study how people infer typicality and atypicality of events from what is said. Large-scale human and LLM experiments show that three CUES predict these inferences in humans: Commonsense Knowledge, Utterance Emphasis and Snark. In most settings LLMs lack the commonsense knowledge to judge an event's general typicality, so they struggle to follow the CUES.

arxiv.orgpaperAdded 27 Oct 2026, published 23 Oct 2026🧠 AI

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.

arxiv.orgpaperAdded 26 Oct 2026, published 23 Oct 2026🛡️ Security

BiLoCo is a post-training quantization method for FP4 LLMs that adds low-rank corrections whose left and right factors are stored as binary ±1 sign vectors instead of BF16. This uses 16x less storage per rank-one component and lets the correction run on CUDA cores alongside the FP4 tensor-core GEMM. On 4B to 72B LLMs it matches or improves memory-matched SVDQuant. A fused B300 kernel is at most 11% slower than FP4-only GEMM at batch size 1 for ranks up to 512.

arxiv.orgpaperAdded 26 Oct 2026, published 23 Oct 2026🧠 AI

This paper studies reverse sycophancy: whether users bend toward a confident, consistent, wrong LLM. In a within-subjects experiment, 32 participants learned science topics from five GPT-5-mini assistants with 0 to 100% of facts replaced by falsehoods. Scores fell from 7.4 to 2.9 out of 10, planted falsehoods were chosen on 60% of falsified items against 11% of correctly taught ones, and confidence stopped tracking correctness. It introduces a baseline-corrected Human Deference Index.

arxiv.orgpaperAdded 24 Oct 2026, published 22 Oct 2026🧠 AI

TTR-Compressor is a sequence compression method that treats input tokens as a test-time regression problem. The regression is learned end-to-end by backpropagation, and its coefficients are solved in closed form in the forward pass to give the compressed representation. As a block-wise sparse attention compressor it improves RULER over attention-based compressors by a large margin. As a visual token merger for multimodal LLMs it beats the MLP merger and matches it with 55.6% fewer visual tokens.

arxiv.orgpaperAdded 24 Oct 2026, published 22 Oct 2026🧠 AI

This empirical study benchmarks recent LLMs against fine-tuned encoder-decoder models on knowledge graph-to-text generation, using reference-based metrics and graph-grounded, reference-free LLM-based evaluation validated against human annotations. Recent LLMs narrow or close the gap on reference-based metrics and do better on graph-grounded ones. Prompted models make fewer contradictions and triple omissions, and fine-tuned models' errors grow faster with graph size.

arxiv.orgpaperAdded 24 Oct 2026, published 22 Oct 2026🧠 AI

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.

arxiv.orgpaperAdded 24 Oct 2026, published 22 Oct 2026🧠 AI

DuoCook is an Overcooked-inspired benchmark in which one vision-language model controls two cooks from top-down views in Unreal Engine environments built with coding agents. It has 20 maps in five categories covering equipment contention, changing terrain and handoff dependencies. Four VLMs were compared; the best, gpt-6-astra, reached 26.2% mean order completion. A notebook memory ablation had mixed effects: positive for Claude and Qwen, negative for GPT and Gemini.

arxiv.orgpaperAdded 24 Oct 2026, published 22 Oct 2026🧠 AI

This paper trains small open language models with reinforcement learning to select tools for multi-step instructions over corpora of more than ten thousand tools. A Qwen3-8B model post-trained with the recipe outperforms Llama-4-Maverick (402B) on ToolBench. The trained model transfers to different retrievers and generalizes to new tool corpora in other domains, without relying on proprietary LLMs like Claude or ChatGPT.

arxiv.orgpaperAdded 24 Oct 2026, published 22 Oct 2026🧠 AI

This paper studies evidence-grounded health summarization for menstrual health monitoring. It combines structured evidence conditioning with RAG in a prompt-based constrained generation framework, and scores 4,500 summaries from three LLMs with NLI, atomic factuality metrics and readability measures. Grounding constraints improved uncertainty preservation and generally kept or improved faithfulness against retrieved evidence, while omission effects varied by model. Clinical alignment came with harder reading and more medical terminology.

arxiv.orgpaperAdded 24 Oct 2026, published 22 Oct 2026🧠 AI

Rate-Controlled Refresh (RCR) is a KV cache re-selection method for multimodal LLMs in long-form generation. Attention drift decides when and where to re-select visual tokens, and a per-sample controller sets how often. RCR beats InfiniGen by 1.6-3.8 ROUGE-L points on five of six model-task settings and stays within 1.7 points of exact per-step re-selection. On video description, where drift is homogeneous, it no longer beats a fixed threshold.

arxiv.orgpaperAdded 24 Oct 2026, published 22 Oct 2026🧠 AI

SecLog-Bench is a benchmark for LLM reasoning over cybersecurity logs, built on an extensible simulation platform covering 29 attack scenarios. It combines audit logs, application logs and network traffic, with attack-stage labels and step-by-step evidence chains. Eight LLMs were tested on post-detection attack-progress reasoning: the best reached 44.44% accuracy, 45.07% Macro-F1 and 54.87% Stage Reasoning F1.

arxiv.orgpaperAdded 23 Oct 2026, published 22 Oct 2026🛡️ Security

SecLog-Bench is a benchmark for LLM reasoning over cybersecurity logs. A simulation platform with 29 attack scenarios generates audit logs, application logs and network traffic, with attack-stage labels and step-by-step evidence chains. Eight mainstream LLMs were tested on attack-progress reasoning: the best reached 44.44% accuracy, 45.07% Macro-F1 and 54.87% Stage Reasoning F1. Correct predictions often came with flawed reasoning.

arxiv.orgpaperAdded 23 Oct 2026, published 22 Oct 2026🧠 AI

The SC-matrix is an evaluation framework for multilingual LLM sycophancy. It crosses a model's competence (correct answer with no pressure) with its sycophancy (keeping that answer under pressure), so agreeing with a false premise can be told apart from not knowing the answer in that language. Results: equal aggregate sycophancy rates hide different failure mixes, high competence does not always protect, and reasoning-oriented mitigations beat directive ones.

arxiv.orgpaperAdded 24 Oct 2026, published 22 Oct 2026🧠 AI

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.

arxiv.orgpaperAdded 24 Oct 2026, published 20 Oct 2026🛡️ Security

InterWebCoder is a framework for interactive webpage code generation by multimodal LLMs. It synthesizes self-contained webpages with executable test scripts from recorded interaction trajectories, repairs failures using runtime evidence, then trains with test-ID-weighted SFT and GRPO using test pass rates and visual similarity as rewards. The pipeline yields 1,736 verified samples from 2,058 tasks. A 9B model scores 48.54 on IWR-Bench and 48.42 on Vision2Web, and reward latency drops over 97%.

arxiv.orgpaperAdded 22 Oct 2026, published 20 Oct 2026🧠 AI

DAR-MGSM (Dialectal Arabic Multilingual Grade School Math) is a manually curated benchmark for mathematical reasoning in Dialectal Arabic. It covers five major Arabic dialects plus Modern Standard Arabic, in a translated-only version and a translated and culturally localized version. Eleven Arabic-centric and multilingual LLMs were tested: accuracy generally drops on the localized version, and a mechanistic interpretability analysis shows the two variants induce distinct internal reasoning trajectories.

arxiv.orgpaperAdded 22 Oct 2026, published 20 Oct 2026🧠 AI

Code2Video-Compass is a benchmark of 2,104 tasks for code-based video generation, where multimodal LLMs write executable programs that produce complete videos from text (T2V), image (I2V) or video (V2V) inputs. Tasks span 10 categories and 24 subtypes. Scoring uses 12 objective metrics and five MLLM-judge metrics. Across 15 proprietary and open-weight models from nine families, performance falls as construction, dynamic animation and state-dependency complexity rise.

arxiv.orgpaperAdded 22 Oct 2026, published 20 Oct 2026🧠 AI

DeclineCraft is a dynamic RAG abstention benchmark whose unanswerable questions target Minecraft feature additions made after a corpus cut-off, with audits for parametric exposure. Across 27 LLMs, single-shot retrieval raises Correct Abstention Rate in 38 of 40 configurations, while iterative RAG lowers it in 30 of 40. Enabling thinking without retrieval lowers abstention for 8 of 13 models, shifting responses to premise denial.

arxiv.orgpaperAdded 22 Oct 2026, published 20 Oct 2026🧠 AI

OpenVLHarness is a multimodal reasoning harness for MLLMs built on a capability layer (task-level tool interfaces plus evidence rendering) and persistent multimodal memory that stores images, structured data and web references as addressable artifacts. A coding interface runs generated programs over stored data. Across 22 benchmarks and eight backbones it adds 9.7-12.6 points over no-tool baselines, and GPT-6 Luna at medium effort beats Codex and Visual Sketchpad at maximum effort using at most 37% of their tokens.

arxiv.orgpaperAdded 21 Oct 2026, published 19 Oct 2026🧠 AI

The paper introduces PhysRSI-eval, a five-axis metric for physical recursive self-improvement: first-attempt performance, efficiency, success, budget and improvement from failure. Several VLMs are tested on 18 manipulation tasks under a fixed interaction budget. It proposes the Physical Barrel Hypothesis and studies observational scaling. Workspace interventions show that removing history lowers late-round success, while frozen workspaces still help on unseen layouts.

arxiv.orgpaperAdded 21 Oct 2026, published 19 Oct 2026🧠 AI

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.

arxiv.orgpaperAdded 21 Oct 2026, published 19 Oct 2026🧠 AI

The paper is a large-scale study of 37 security skills run through Claude Code with four LLMs on 100 real Linux kernel vulnerabilities, 14,800 runs in all. Only 24.4 percent of runs found the correct function and 15.7 percent classified the vulnerability correctly. It proposes SKILL-SIF, which evolves skills from failure patterns and raised fully correct detection on 100 unseen vulnerabilities from 2 to 16 percent.

arxiv.orgpaperAdded 21 Oct 2026, published 19 Oct 2026🧠 AI

Empirical study of 37 security skills run through Claude Code with four LLMs on 100 real Linux kernel vulnerabilities across ten classes (14,800 runs). 24.4% of runs found the correct function, 18.3% the function and statement, and 15.7% the vulnerability class. The authors propose SKILL-SIF, which evolves skills from failures and raised fully correct detection on 100 unseen vulnerabilities from 2% to 16%.

arxiv.orgpaperAdded 21 Oct 2026, published 19 Oct 2026🛡️ Security

OwlSight is a chain-of-thought monitor that uses "Factor", a monitoring requirement written into the prompt, so one monitor can cover many tasks across capability, safety and trustworthiness. Factors come at fine granularity (a specific leaked email address) and coarse granularity (privacy leakage). On 15,971 reasoning trajectories it beats general-purpose LLMs by 1.32-30.57 points (fine) and 11.71-27.81 points (coarse), and reaches state-of-the-art on OpenAI's CoT monitorability benchmark. It generalizes to unseen models and resists Factor rephrasing, CoT compression and obfuscation.

arxiv.orgpaperAdded 20 Oct 2026, published 16 Oct 2026🧠 AI

This paper builds a benchmark of 500 debatable questions across ten domains to measure how user narratives steer LLM agreement. Each question has 11 matched conditions (one neutral, five mirrored narrative levels per opposing stance), giving 5,500 instances across seven LLMs. Causal framing and worldview synthesis raise agreement, personal experience can lower it, and GPT-5.1 ablations show bullet formatting also increases agreement.

arxiv.orgpaperAdded 20 Oct 2026, published 16 Oct 2026🧠 AI

LexPilot is a benchmark linking proactive legal interaction to claim-level verdict prediction, built from 432 real Chinese civil judgments across 36 causes of action. It pairs expert-reviewed complete and underspecified consultation histories with court verdicts. Current LLMs score on average 23.84% lower on judgment prediction in underspecified consultations than in fully specified ones, and the paper analyses the bottlenecks.

arxiv.orgpaperAdded 20 Oct 2026, published 16 Oct 2026🧠 AI

EDIC is a decomposed reward framework that trains LLMs to decide, for each instruction embedded in external data such as documents or tool returns, whether to follow or reject it. A judge initialized with SFT and refined with GRPO attributes compliance per instruction and yields separate rewards for beneficial and harmful compliance. On Qwen3-4B it raises harmful-rejection by up to 7.7% and Arena-Hard-Auto utility by 12.5% over the strongest prior defense.

arxiv.orgpaperAdded 20 Oct 2026, published 16 Oct 2026🧠 AI

EDIC is a decomposed reward framework for per-instruction compliance in LLMs that read external data, where one document or tool return mixes legitimate directives with injected ones. A generative judge, trained with SFT then GRPO, attributes compliance per instruction and produces RL rewards for beneficial and harmful compliance. On Qwen3-4B it raises harmful rejection by up to 7.7% and Arena-Hard-Auto utility by 12.5% across five benchmarks.

arxiv.orgpaperAdded 20 Oct 2026, published 16 Oct 2026🛡️ Security

R^2ECAP (Reused Reasoning-Cache Poisoning) is a black-box attack that plants poisoned reasoning traces through a chat interface, exploiting LLM systems that reuse KV caches of reasoning. A preceding trace steers the model, and the retained trace keeps residual adversarial influence in its cache after the preceding trace is discarded. On three open-weight LLMs and three datasets it reaches up to 79% ASR, beats the white-box HijackKV in 8 of 9 settings, and reaches 46% ASR in a reasoning-trace retrieval system. Only 1% of retained responses are flagged by a popular prompt injection detector.

arxiv.orgpaperAdded 20 Oct 2026, published 16 Oct 2026🛡️ Security

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.

huggingface.copaperAdded 16 Oct 2026🧠 AI

Introduces Librarian Agents, where a builder agent turns raw unstructured documents into a hierarchical filesystem of synthetic files and reference links, forming a workspace for a downstream answering agent. Workspace construction is cast as a Markov decision process and the builder policy is trained with reinforcement learning to maximize downstream answering accuracy. It beats the strongest retrieval and precomputation baselines by 33 points on BrowseComp-Plus, amortizes construction cost over repeated queries, and generalizes zero-shot to BRIGHT with a 31-point average gain.

arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026🧠 AI

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.

arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026🧠 AI

GraphACM merges equivalent states from multiple rollouts of the same LLM agent task into a directed, action-labeled graph, so actions can be compared at shared decision states. It defines node-level and global action-consistency measures, and uses the global score as an uncertainty proxy to predict task success or failure. Across three LLMs on ALFWorld, AppWorld and WebShop it reaches an average AUROC of 0.811, above the strongest behavioral-consistency baseline (0.729) and verbalized confidence (0.769). It also shows where in a trajectory agent actions diverge.

arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026🧠 AI

GACC-Bench is a benchmark for Goal-Adaptive Context Compression, testing how well LLMs retain goal-relevant information when compressing a source context under a length budget before downstream questions are known. It has 500 goals and 2,571 human-reviewed questions over 281 document and agent-trace contexts from seven collections; a fixed reader answers the withheld questions from the compressed context. Ten LLM compressors show large gaps to full-context performance, especially in answering every question tied to a goal. Target-goal conditioning improves accuracy over goal-agnostic compression, while mismatched goals hurt it.

arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026🧠 AI

INFUSE is an end-to-end benchmark for indirect prompt injection (IPI) across long-horizon agent workflows (shell commands, code changes, dependency installs, remote services). It has 40 realistic tasks and a taxonomy of 56 attack scenarios in five threat categories, with attacks injected via interaction surfaces encountered mid-task. Across 19 LLMs (GPT, Claude, Gemini, Llama, Gemma, Qwen) and 41,824 adversarial trajectories, mean attack success was 19.6% (LLM-judged) and 15.4% (heuristic), per-model 3.2–41.9%; guard models gave limited protection, while prompt-level hardening cut mean ASR from 58.2% to 14.3%.

arxiv.orgpaperAdded 17 Oct 2026, published 14 Oct 2026🛡️ Security