Hallucinated — page 5

DualEval: Joint Model-Item Calibration for Unified LLM Evaluation (arxiv.org)

9h

Current LLM evaluation relies on two complementary but often disconnected signals: static benchmarks with objective correctness labels and arena-style preference data that better reflect open-ended user interactions. We introduce DualEval,…
Staying VIGILant: Mitigating Visual Laziness via Counterfactual Visual Alignment in MLLMs (arxiv.org)

9h

Multimodal large language models (MLLMs) extend large language models (LLMs) with visual perception, enabling joint reasoning over images and text. Despite inheriting strong reasoning capabilities from LLMs, they remain prone to hallucinat…
LLM-Based Examination of Eligibility Criteria from Securities Prospectuses at the German Central Bank (arxiv.org)

9h

Verifying the eligibility of securities as collateral is a key responsibility of the German Central Bank. However, manually verifying these assets against legal and financial criteria within lengthy, semi-structured, and often bilingual pr…
Beyond Surface Forms: A Comprehensive, Mechanism-Oriented Taxonomy of Indirect Linguistic Encoding for LLM-Based Coded Language Detection (arxiv.org)

9h

To avoid moderation and surveillance on social media, some users routinely invent indirect linguistic expressions (ILE) that camouflage sensitive meanings. Such expressions surface as algospeak, euphemisms, and adversarial obfuscation, dep…
Paved with True Intents: Intent-Aware Training Improves LLM Safety Classification Across Training Regimes (arxiv.org)

9h

We argue that safety classifiers should model user intent as an explicit signal between the prompt and the final label. To study this, we introduce AIMS, a human-annotated dataset of 1,724 difficult safety prompts, each paired with an inte…
Forecasting With LLMs: Improved Generalization Through Feature Steering (arxiv.org)

9h

Successful forecasting involves identifying patterns between historical and future states of the world which generalize to future observations. We apply LLMs to a variety of forecasting tasks and inspect their internal states using sparse…
Improving General Role-Playing Agents via Psychology-Grounded Reasoning and Role-Aware Policy Optimization (arxiv.org)

9h fine-tuning

Building general-purpose role-playing agents that faithfully portray any character from a natural-language profile remains challenging. The dominant paradigm -- supervised fine-tuning -- encourages behavioral mimicry without deep, human-li…
MinGram: A Minimalist Unigram Tokenizer with High Compression and Competitive Morphological Alignment (arxiv.org)

9h

The Unigram tokenizer uses an elegant representation which makes it straightforward to edit vocabularies, but its training is comparatively heavy and complex. We introduce MinGram (Minimalist Unigram), which keeps the token-list representa…
Cascaded Multi-Granularity Pruning for On-Device LLM Inference in Industrial IoT (arxiv.org)

9h

Deploying large language models (LLMs) on Industrial Internet of Things (IIoT) edge devices demands extreme compression, yet existing structured pruning methods collapse at high compression ratios due to one-shot importance estimation, and…
OPID: On-Policy Skill Distillation for Agentic Reinforcement Learning (arxiv.org)

9h agentic

Outcome-based reinforcement learning provides a stable optimization backbone for language agents, but its sparse trajectory-level rewards provide little guidance on which intermediate decisions should be reinforced or suppressed. On-policy…
Closing the Quality Gap in Low-Resource Text-to-Speech: LoRA Fine-Tuning of VoxCPM2 for Khmer and Korean (arxiv.org)

9h fine-tuning

Large pretrained text-to-speech (TTS) models sound almost human for well-resourced languages, but much worse for languages that are rare in their training data. We study this quality gap for Khmer and Korean using VoxCPM2, a 2.4B-parameter…
Erase-then-Delta Attention: Decoupling Erase and Write Addresses in Delta-Rule Linear Attention (arxiv.org)

9h

Delta-rule linear attention improves recurrent memory updates by correcting what is already stored at the current write address before writing new content. However, the active correction is still anchored to that same write address.
Comparing BERT Sentence-Pair Classification and Few-Shot LLM Prompting for Detecting Threat and Solution Framing in German Climate News (arxiv.org)

9h

News media play a central role in shaping public perceptions of climate change, and whether coverage emphasizes threats or solutions has measurable effects on audience engagement and policy support. Automated detection of these framing pat…
Extracting Problem and Method Sentence from Scientific Papers: A Context-enhanced Transformer Using Formulaic Expression Desensitization (arxiv.org)

9h

Billions of scientific papers lead to the need to identify essential parts from the massive text. Scientific research is an activity from putting forward problems to using methods.
Soft Token Alignment for Cross-Lingual Reasoning (arxiv.org)

9h

Multilingual large language models often produce inconsistent reasoning and answers for semantically equivalent prompts in different languages. Prior work suggests that intermediate representations can be relatively language-agnostic, but…
ProfileFoundry: A Synthetic Person-Object Substrate for Privacy, Memory, and Tool-Use Evaluation in LLM Agent (arxiv.org)

9h

Foundation-model research increasingly needs data about people: user state, personal histories, relationships, contact-like fields, documents, and longitudinal updates. Real user data is difficult to share, perturb, audit, or redistribute…
Dynamic-dLLM: Dynamic Cache-Budget and Adaptive Parallel Decoding for Training-Free Acceleration of Diffusion LLM (arxiv.org)

9h

Diffusion Large Language Models (dLLMs) offer a promising alternative to autoregressive models, excelling in text generation tasks due to their bidirectional attention mechanisms. However, their computational complexity scales on the order…
Hierarchical Fault Detection and Diagnosis for Transformer Architectures (arxiv.org)

9h

Transformers now underpin critical AI systems across industry and research. Yet their faults can silently alter model behavior without runtime errors, and existing techniques offer little support for tracing these failures to their compone…
TransXion: A High-Fidelity Graph Benchmark for Realistic Anti-Money Laundering (arxiv.org)

9h

Money laundering poses severe risks to global financial systems, driving the widespread adoption of machine learning for transaction monitoring. However, progress remains stifled by the lack of realistic benchmarks.
A Guideline-Aware AI Agent for Zero-Shot Target Volume Auto-Delineation (arxiv.org)

9h

Delineating the clinical target volume (CTV) in radiotherapy involves complex margins constrained by tumor location and anatomical barriers. While deep learning models automate this process, their rigid reliance on expert-annotated data re…
Improved Bounds for Private and Robust Alignment (arxiv.org)

9h

In this paper, we study the private and robust alignment of language models from a theoretical perspective by establishing upper bounds on the suboptimality gap in both offline and online settings. We consider preference labels subject to…
Pianist Transformer: Towards Expressive Piano Performance Rendering via Scalable Self-Supervised Pre-Training (arxiv.org)

9h

Existing methods for expressive music performance rendering, a conditional generation task that aims to generate a human-like performance from a symbolic score, rely on supervised learning over small labeled datasets, which limits scaling…
Reconstruction Alignment Improves Unified Multimodal Models (arxiv.org)

9h

Unified multimodal models (UMMs) unify visual understanding and generation within a single architecture. However, conventional training relies on image-text pairs (or sequences) whose captions are typically sparse and miss fine-grained vis…
Library Drift: Diagnosing and Fixing a Silent Failure Mode in Self-Evolving LLM Skill Libraries (arxiv.org)

9h

Self-evolving skill libraries face a silent failure mode we term \emph{library drift}: unbounded skill accumulation without outcome-driven lifecycle management causes retrieval degradation, false-positive injections, and performance stagna…
Evaluating Deep Research Agents on Expert Consulting Work: A Benchmark with Verifiers, Rubrics, and Cognitive Traps (arxiv.org)

9h agentic

Frontier deep research agents (DRAs) are being deployed in enterprise workflows faster than they are being evaluated. Existing benchmarks measure factual recall, single-hop QA, or generic agentic skill, and miss the multi-document, decisio…
Experience Compression Spectrum: Unifying Memory, Skills, and Rules in LLM Agents (arxiv.org)

9h

As LLM agents scale to long-horizon, multi-session deployments, efficiently managing accumulated experience becomes a critical bottleneck. Agent memory systems and agent skill discovery both address this challenge, extracting reusable know…
Understanding Domain-Aware Distribution Alignment in Budgeted Entity Matching (arxiv.org)

9h

Entity Matching (EM) is a core operation in the data integration pipeline, where records from different sources are compared to determine whether they refer to the same real-world entity. Recent work has incorporated domain information and…
Empowering GUI Agents via Autonomous Experience Exploration and Hindsight Experience Utilization for Task Planning (arxiv.org)

9h

Multimodal web agents can assist humans in operating repetitive GUI tasks, where effective task planning is essential for decomposing complex tasks into executable actions. While small open source MLLMs are cost efficient and privacy prese…
Advancing Omnimodal Embodied Agents from Isolated Skills to Everyday Physical Autonomy (arxiv.org)

9h

Building persistent embodied agents in unstructured environments demands unified orchestration of heterogeneous tools spanning both cyber (APIs, IoT) and physical (manipulation, navigation) domains, coupled with autonomous recovery from ph…
CARVE: Content-Aware Recurrent with Value Efficiency for Chunk-Parallel Linear Attention (arxiv.org)

9h

Recurrent models must forget in order to remember, yet the state of the art decides what to erase without consulting what is stored -- the gate sees only the arriving token, not the memory it is about to modify. This memory-blind gating is…