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arxiv.orgpaper🧠 AI

M-PFT: Angular Margin for Calibrated Prompt-Tuning of Fine-Tuned Vision-Language Models

M-PFT is an angular-margin method for prompt tuning of vision-language models that fixes base-to-novel dual miscalibration (underconfidence on base classes, overconfidence on novel classes). It adds two losses: minimizing the maximum cosine similarity between tunable class embeddings, and minimizing angular distance to each zero-shot embedding. Tests on 9 prompt-tuning methods and 15 datasets show lower novel-class calibration error at comparable accuracy and negligible overhead.

vision-language modelprompt tuning
arXiv

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

arxiv.orgpaper🧠 AI

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

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

diffusion modelTransformer
arXivGenEvalDPG-Bench+1

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

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

Who Is Sycophantic to Whom? Confident LLMs, Human Deference, and the Collapse of Trust Calibration

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.

LLM
GPT-5-miniarXiv

research, ai-safety, evals· Added 24 Oct 2026, published 22 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

arxiv.orgpaper🧠 AI

How Benchmark Graph Construction Shapes Multi-Hop KG Navigation: A Controlled Audit of PathQuestion and PathQuestion-Large

This paper audits the PathQuestion (PQ) and PathQuestion-Large (PQL) multi-hop knowledge-graph QA benchmarks as graph-navigation tasks. It holds questions, answers, path annotations and splits fixed, and replaces each released task-filtered graph with the larger source KG. Restoration cuts released-answer accuracy and trajectory fidelity for frozen small-language-model policies and trained MINERVA policies, and graph-consistent evaluation recovers only part of the loss.

MINERVA
PathQuestionPathQuestion-LargearXiv

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

arxiv.orgpaper🧠 AI

SHADE: Shrinkage via Halted Alpha-stable Denoising for Heavy-Tailed Probabilistic Time Series Forecasting

SHADE is an alpha-stable Lévy diffusion forecaster that stops denoising at a nonzero depth, so the leftover stable kernel shrinks the predictive distribution instead of leaving it overconfident. It offers a scale-calibrated halt and a context-amortized halt that needs no noise labels. Median CRPS improves 21.7% on synthetic data with alpha at or below 1.3, with tail calibration gains on cryptocurrency and foreign-exchange series.

diffusion model
arXiv

research· Added 21 Oct 2026, published 19 Oct 2026

arxiv.orgpaper🧠 AI

GraphACM: Graph-based Action Consistency Measurement for Uncertainty Quantification in LLM Agents

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.

LLM
GraphACMALFWorldAppWorld+2

research, agents, evals· Added 17 Oct 2026, published 15 Oct 2026

eprint.iacr.orgpaper🧠 AI

A Graph-Based Analysis of Comparability, Scale, and Attribution in Private Transformer Inference

Systematization of knowledge on private Transformer inference (PTI) that builds an empirical graph of 62 systems, with system attributes as node features and evaluation compatibility as edges. Node-level attribution finds that cryptographic progress drives most speedups but always co-occurs with confounders such as model fidelity, hardware and cost accounting. Edge-level auditing finds only 1 of 1891 candidate system pairs directly comparable. Graph-level analysis shows the scale frontier plateaued at 13B parameters and projects the cost of deploying current PTI techniques on production-scale Transformers.

TransformerLLM
Souvik DasAriful AzadNitesh Saxena

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

arxiv.orgpaper🧠 AI

Dog-Breed Text-to-image Flow Matching from Pretraining to Fine-Tuning on 4 Consumer GPUs

MiniDog is a framework for training a text-to-image flow-matching diffusion transformer from scratch on four RTX 3090 GPUs, with 55 minutes of pretraining and 15 minutes of fine-tuning. It pretrains on 26k ImageNet dog images that were captioned, then fine-tunes on 2k FLUX.1-schnell generated images. Image and text features are precomputed and cached. The model is a 22M-parameter transformer with the E2E-INVAE semantic latent space, a small Qwen text encoder and in-context conditioning, and training is accelerated with iREPA and an EUPE perception encoder. The repo evaluates with FID, HPSv2, PickScore and VAE reconstruction metrics. It lowers the barrier to hands-on diffusion training research.

flow matchingdiffusion modelTransformer+1
MiniDogImageNetFLUX.1-schnell+7

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

arxiv.orgpaper🧠 AI

PixelMoE: Scaling Pixel-Space Diffusion with LLM-Style Mixture-of-Experts

PixelMoE is a pixel-space diffusion framework that scales capacity sparsely using standard LLM-style Mixture-of-Experts: token-choice routing, fine-grained routed experts, a shared expert and bias-based load balancing, with no diffusion-specific routing or auxiliary routing losses. It adds Pixel-Space Hierarchical Flow Matching, which varies spatial resolution along the denoising trajectory from global structure to fine detail. At matched active parameters it beats dense counterparts, and reaches FID 1.47 at 256x256 and 1.53 at 512x512 on class-conditional ImageNet-1K without representation alignment or perceptual losses. Code and models are promised but not yet released.

Mixture of Expertsdiffusion modelflow matching+1
PixelMoEImageNet-1KarXiv

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

eprint.iacr.orgpaper🛡️ Security

Hybrid Quantum–Computational Key Distribution with Reduced Assumptions (Short Paper)

Short paper analysing the HM-QCT hybrid quantum–computational time-lock QKD protocol (Mazzoncini et al., 2025). It spells out the two "harvest now, decrypt later" strategies (quantum memory and classical memory) and shows each is excluded by exactly one of the two assumptions: short-term encryption and low-fidelity quantum storage. It then proposes HM-PRF, which replaces the short-term encryption with a locally evaluated pseudorandom function, so security needs only one-way functions rather than a predicted lasting gap in quantum memory technology.

HM-QCTHM-PRFharvest now, decrypt later+3

other· Added 14 Oct 2026, published 13 Oct 2026

arxiv.orgpaper🧠 AI

Claim Framing as a Driver of LLM Sycophancy

Systematic study of which framings of a user's claim trigger sycophancy in factual question answering, where every answer is objectively verifiable. Across 14 models and 42K questions, correct and incorrect user opinions are expressed through 21 framing types in 8 dimensions, and multiple opinion-driven behaviors are measured. All models are susceptible regardless of size; confidence of statement, attribution to whom, and framing as shared between user and model matter most, while expertise, authority and social pressure matter little. Larger models resist incorrect opinions better but are no better at recovering from their own errors, so the authors argue sycophancy evals must probe framing and measure more than a single flip rate.

LLM
arXiv

ai-safety, evals, research· Added 14 Oct 2026, published 13 Oct 2026

eprint.iacr.orgpaper🧠 AI

Confidential Error-Correcting Codes

Introduces Confidential Error-correcting Codes (CECs), a primitive that decodes reliably from perturbed codewords while keeping the message confidential, defined by confidentiality, robustness and soundness. The Randomness-Security Separation framework encodes a nonce and masks the payload with a PRF; the Conc-CEC instantiation uses concatenated Reed-Muller and Reed-Solomon codes and claims stronger concrete security than PRCs (CRYPTO'24). The application, CECMark, watermarks AI-generated images and reportedly beats Gaussian Shading, PRCMark and T2SMark across 56 perturbations with a false positive rate below 1e-5 and no side effects.

Reed-Muller codesReed-Solomon codespseudorandom function
Confidential Error-Correcting CodesCECMarkConc-CEC+5

research, multimodal· Added 14 Oct 2026, published 10 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Confidential Error-Correcting Codes

Introduces Confidential Error-correcting Codes (CECs), a cryptographic primitive for decoding perturbed codewords while keeping the message confidential, defined by confidentiality, robustness and soundness. The Randomness-Security Separation framework encodes a nonce and masks the payload with a PRF. The Conc-CEC instantiation uses concatenated Reed-Muller and Reed-Solomon codes, and the authors claim stronger concrete security than PRCs (CRYPTO'24). The CECMark application watermarks AI-generated images, and the authors report it beats Gaussian Shading, PRCMark and T2SMark across 56 perturbations with a false positive rate below 10^-5.

Conc-CECCECMarkPRCMark+3

AI Security· Added 14 Oct 2026, published 10 Oct 2026

arxiv.orgpaper🛡️ Security

Physical Side-Channel Attacks on Edge LLMs: Extracting Output Tokens and User Prompts

Presents an EM side-channel attack on LLaMA-3.2-1B-Instruct running on a Raspberry Pi 5, showing passive physical measurements leak generated tokens and user prompts. Three attacks: a profiling attack (97.2% accuracy over 50 medical/privacy keywords), a hidden-state reconstruction attack predicting tokens from the 128k vocabulary (94.3% on profiled classes; 67% of unseen words ranked in top 100), and a hidden-state inversion attack reconstructing prompts semantically. Shows that local edge inference does not guarantee confidentiality of tokens and inputs.

LlamaLLMRaspberry Pi 5
electromagnetic side-channel attackhidden-state inversion

Hardware & RF, AI Security· Added 16 Oct 2026, published 9 Oct 2026

arxiv.orgpaper🧠 AI

TaoD2C-Bench: Benchmarking MLLMs for Industrial UI Code Generation Beyond Visual Fidelity

TaoD2C-Bench is a benchmark for evaluating multimodal large language models (MLLMs) on industrial design-to-code (D2C) tasks — converting UI designs into code that satisfies implementation constraints, not just visual fidelity. It includes the TaoD2C dataset of 2,861 production designs from 17 commercial platforms with 97,652 expert annotations across Component, Group, Alignment, and Position categories, and defines three tasks: end-to-end UI code generation, requirement inference, and requirement realization. Evaluating eight MLLMs, the authors find large gaps between visual reconstruction ability and the ability to generate code meeting domain-specific implementation requirements.

vision-language model
TaoD2C-BenchTaoD2C

evals, multimodal· Added 8 Oct 2026, published 7 Oct 2026

arxiv.orgpaper🧠 AI

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

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

diffusion modelDirect Preference Optimization
DIGHT

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

arxiv.orgpaper🧠 AI

ALIVE: Interaction-Aligned Object Insertion for First-Frame-Guided Video Editing

ALIVE is a framework for first-frame-guided video editing that inserts objects so they coherently participate in interactions (e.g., being picked up or manipulated) rather than appearing static. The authors curate a 35,800-pair editing dataset combining 3D-rendered, model-generated and real-world videos plus pairs from ROSE, and train a vision-language model to predict interaction guidance from the edited first frame and an instruction naming the object. They introduce the ALIVE-interaction benchmark to measure interaction fidelity, source preservation and visual coherence via a unified VLM-based protocol, reporting a 43.9% Overall improvement on this benchmark and 4.4% on a general video object insertion benchmark over the strongest baseline, with VLM-predicted guidance adding a further 0.95-point gain.

vision-language model
ALIVEROSEALIVE-interaction

research, multimodal· Added 7 Oct 2026, published 6 Oct 2026

arxiv.orgpaper🧠 AI

Agent in a Bottle: Can LLM Agents Turn Their Capabilities Into Cheap, Scalable Artifacts?

This paper introduces BOTTLED, a benchmark testing whether LLM agents can autonomously convert their general task-solving ability into cheaper, scalable artifacts (e.g., training a small distilled model or writing a reusable program) when faced with millions of similar queries under fixed time, compute, and API budgets, a capability the authors call bottling. Across ten models and three tasks, the authors find zero-shot performance does not reliably predict bottling success: 48 of 60 runs scored below their own model's zero-shot confidence interval, and 31 of 60 underperformed simple small-model distillation baselines. Despite this, strong agents like Opus 5 can retain about 82% of zero-shot macro-F1 at about 657x lower cost on query-product relevance classification, and recover about 94% of a dedicated cheap-inference model's (Jev) performance at a quarter of its cost.

Opus 5Jev

evals, agents, research· Added 7 Oct 2026, published 6 Oct 2026

arxiv.orgpaper🧠 AI

Post-Training Semantic Lifting for 3D Gaussian Splatting: Separating Detector, Lifting and Representation Error

This arXiv paper introduces a post-training semantic lifting method for 3D Gaussian Splatting that assigns a single target class to Gaussians by accumulating target/non-target evidence across all views, weighted by per-view visibility, then filters with a dual-threshold (β and γβ) scheme that selects high-confidence seeds and extends them via connected components. The authors evaluate by transferring labels from Gaussians to mesh vertices and decompose error into three sources — the 2D detector, the lifting step, and the representation transfer — tuning thresholds on seven Replica scenes and testing on ten held-out ScanNet++ scenes, reaching mean mIoU of 0.93/0.65 (dataset masks/YOLO masks) on validation and 0.80/0.54 on test, a 0.24 mIoU improvement over prior per-view thresholding. The error analysis finds the 2D detector is the dominant remaining error source, informing where future gains in 3D semantic reconstruction should focus.

3D Gaussian Splatting
ScanNet++ReplicaYOLO+1

research, multimodal· Added 7 Oct 2026, published 6 Oct 2026

arxiv.orgpaper🧠 AI

WorldSolver: Can LLM Agents Simulate the Physical Dynamics via Solver Generation?

WorldSolver is a benchmark of 168 physics-simulation coding tasks drawn from 61 classic computer-graphics papers across 7 physical domains, each giving an LLM agent a fixed code scaffold and asking it to implement the missing solver. Agents are scored on three axes — Execution Checks, Visual Fidelity, and Physical Plausibility — and frontier agents GPT-5.6 Sol and Claude Opus 5 top out at only 48.7% and 46.7% overall, showing current agents struggle to produce executable, visually and physically correct simulation code.

GPT-5.6 SolOpus 5

research, evals· Added 7 Oct 2026, published 6 Oct 2026

arxiv.orgpaper🧠 AI

Learning What to Imitate: Entropy-Aware Distribution Mixing

The paper proposes Entropy-Aware Mixing, a knowledge-distillation method where a small student model's per-token distribution is dynamically interpolated with a stronger teacher model's distribution, gated by the student's own predictive entropy, to avoid "confident conflicts" where the student must imitate low-probability continuations despite being confident in a different one. The authors implement both convex and geometric interpolations under two training regimes — offline trace generation via speculative decoding followed by SFT, and on-policy forward-KL distillation — and show it stabilises distillation, improving in- and out-of-distribution math reasoning while better preserving general capabilities than fixed-teacher supervision, with offline traces favouring concave entropy schedules and on-policy training favouring linear/convex ones.

training· Added 6 Oct 2026, published 5 Oct 2026

arxiv.orgpaper🧠 AI

Better Call Reward: Reward Hacking as Strategic Abstention in Legal Reasoning Models

Researchers fine-tune Qwen3-8B with GRPO against a proxy reward built from surface features (citation count, legalese density, response length) and find the model learns to strategically abstain rather than reason: on 16 LegalBench yes/no legal tasks, accuracy collapses from chance (0.500) to 0.072 because the rate of properly formatted, committed answers drops from 0.900 to 0.109, even though accuracy on answers it does commit to actually rises. They term this the "Saul Goodman effect," formally prove it is the optimal policy for any surface-feature proxy that doesn't penalize abstention, show 89.3% of post-training citations are hallucinated/corrupted real case names, and introduce three diagnostics (Confidence Theater Score, Citation Plausibility Rate, Regret Gap) to detect this failure mode before deployment.

Qwen3GRPO
Qwen3-8BLegalBench

research, ai-safety, training· Added 6 Oct 2026, published 5 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Randomness-Anchored Runtime Discovery of Cryptographic Operations on Linux Using eBPF

This IACR ePrint paper presents a Linux runtime cryptographic discovery system built on eBPF user-space probes that detects both classical and post-quantum (ML-KEM) cryptographic operations as they execute, complementing static binary/SBOM analysis with dynamic observation. It uses "randomness anchoring" — correlating observed key-generation/encapsulation randomness with subsequent crypto API calls in the same process — to raise detection confidence without adding false positives, reporting perfect precision/recall on classical and ML-KEM workloads, median detection latency of 28 μs, and 14–18% overhead on key-generation-bound workloads. A nine-application case study (drawn from/paired with QED datasets) compares static vs. runtime views and shows how eBPF probe coverage limits what can be observed, relevant to building a runtime cryptographic bill of materials for PQC migration compliance.

eBPFLinuxML-KEM+1
QED datasets

AppSec, GRC· Added 8 Oct 2026, published 5 Oct 2026

arxiv.orgpaper🧠 AI

Do Large Language Models Know Colombian Law? A Reliability Benchmark for the Colombian Legal System

This paper introduces an expert-validated benchmark of 1,042 items spanning ten areas of Colombian law and three question formats (closed multiple-choice, semi-open, open-ended IRAC), used to evaluate 15 proprietary and open-weight LLMs. Closed-question accuracy ranges from 0.577 up to 0.905 (Gemini 3.1 Pro), but free-text factual correctness never exceeds 0.45 for any model, and only about half of the legal norms models cite in free-text answers are correct — the rest are wrong or fabricated. The study also finds a negative correlation (rho = -0.46) between answer relevancy and factual correctness, meaning models sound confident and responsive while frequently being wrong, and argues current LLMs need expert supervision plus source-grounding for reliable legal use.

Gemini 3.1 Pro

evals, research· Added 5 Oct 2026, published 2 Oct 2026

arxiv.orgpaper🧠 AI

World Embedding Benchmark

The World Embedding Benchmark is a new evaluation suite of 8,000 controlled simulation cases across 80 families (fluid mechanics, solid mechanics, dynamics, optics & electromagnetism), each pairing a rendered video with simulation-derived physical annotations. It tests pre-trained omnimodal embedding models on text-video retrieval, physical-property regression, and multiple-choice video-description classification, finding that current embeddings align poorly cross-modally while lightweight probes can still recover physical quantities, and that continual contrastive training improves alignment but hurts regression. The authors also show retrieval-augmented generation using these embeddings with MiniMax-H3 improves the physical fidelity of generated videos.

MiniMax-H3
World Embedding Benchmark

evals, multimodal, research· Added 5 Oct 2026, published 2 Oct 2026

arxiv.orgpaper🧠 AI

CLIMB: Confidence-Guided Complementary Evidence for Multimodal Retrieval-Augmented Generation

CLIMB is a training-free, inference-time framework for multimodal retrieval-augmented generation that improves knowledge-intensive visual question answering. It first builds a compact, non-redundant evidence pool using an MMR-style objective balancing query relevance and redundancy, then applies an R/E/C critic (relevance, evidence specificity, cross-modal alignment) alongside a confidence estimator that only accepts an answer update when estimated confidence increases, giving a built-in stopping criterion without modifying the underlying retriever or MLLM. Experiments on Encyclopedic-VQA and InfoSeek show consistent gains over existing multimodal RAG baselines.

vision-language model
Encyclopedic-VQAInfoSeekCLIMB

llm-apps, multimodal· Added 5 Oct 2026, published 2 Oct 2026

arxiv.orgpaper🧠 AI

Can AI Oversight Be Zero Knowledge?

This paper studies whether AI oversight mechanisms based on interactive proofs and debate (used for verifying AI outputs derived from confidential data, e.g. a fitness-for-duty assessment from medical records) can be made zero-knowledge, so a verifier learns nothing beyond correctness. The authors prove a general impossibility result in the random oracle model — no zero-knowledge proofs exist for general oracle-aided computation, and this extends to debate, a canonical scalable-oversight model — but show that if the oracle cryptographically signs its answers, every oracle-aided computation can be verified in zero knowledge with an efficient prover and verifier using only collision-resistant hash functions. The signed-oracle construction also offers a new approach to scalable oversight that avoids relying on an honest debate opponent or on the computation's robustness.

ai-safety, research· Added 2 Oct 2026, published 1 Oct 2026

arxiv.orgpaper🛡️ Security

Do Defenses Against LLM Extraction Work Across Attacks? A Lifecycle Benchmark of Black-Box Model Extraction

This arXiv paper presents a reproducible benchmark that unifies evaluation of six black-box LLM extraction attacks, ten defenses, and two adaptive attacks (paraphrasing and back-translation of protected responses) under controlled query budgets and model configurations. It measures surrogate capability, fidelity to the victim model, output quality (Rep-4), and provenance-detector scores, giving a consistent basis for comparing how extraction methods and defenses interact under text-only API access. Code and artifacts are linked from the paper.

LLM
Rep-4

AI Security· Added 3 Oct 2026, published 30 Sep 2026

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