grounding

ReGround is a large-scale dataset for grounding reviewer comments in multimodal evidence from scientific papers, revealing the challenges and importance of integrating text, tables, and figures in retrieval tasks.

This paper introduces a graph-grounded harness for vision-language models to improve accuracy in answering topology questions about Piping and Instrumentation Diagrams by recovering evidence graphs from images.

This paper audits scene-level confabulation in LLM-generated autobiography against a documented ground-truth corpus, finding a 96.7% verification-failure rate and contributing a reusable audit instrument and a grounding remedy.

Introduces a rubric-based reinforcement learning method to improve visual faithfulness in vision-language models by decomposing responses into atomic propositions and scoring them on visual faithfulness, reasoning consistency, and instruction following.

ExtractBench is a new benchmark for document extraction that strictly scores grounding accuracy, showing VLMs and coding agents lack evidence while LlamaExtract Agentic Plus leads with high page-level and word-level performance.

This paper studies the trade-off between grounding and coverage in long-form hallucination reinforcement learning, proposing rubric-based rewards to represent required and optional information for questions. A soft combination of grounding, rubric coverage, and relevance yields the best balance between support and richness.

LiquidAI releases LFM2.5-VL-3B, a 3B multimodal model for on-device deployment with improved OCR, grounding, and efficient inference, available in multiple formats including GGUF, ONNX, and MLX.

LiquidAI announces LFM2.5-VL-3B, an efficient vision-language model for edge hardware with improved screen understanding, grounding, multi-image input, and function calling, trained with 4x more vision data and post-training via SFT and RL.

Search-G1 proposes a representation-based intrinsic reward framework for search-augmented language agents, using intervention-calibrated readouts to balance retrieval necessity and evidence reliance, improving search efficiency without costly annotations.

This paper introduces a concept-centric benchmark to probe LLMs' understanding of geo-spatial concepts like direction, distance, and topology, testing abstraction, compositionality, and grounding across various model architectures and scales. Findings reveal clear limitations in current LLMs' conceptual understanding.

This paper introduces Counterfactual Evidence Disentanglement (CED), a training-time method that makes vision-language models rely on concrete image evidence rather than language priors or shortcuts, improving visual reasoning grounding across benchmarks.

GeoArbiter proposes a training-free pipeline that selectively injects image-unverifiable geographic facts into remote-sensing multimodal LLMs to reduce knowledge hallucinations while preserving retrieval accuracy gains.

The article argues that better semantic search or larger context windows won't fix unreliable AI agents; instead agents must verify retrieved context by reopening the original source before answering or acting.

The article recounts an incident where an AI assistant wrongly confirmed a service, and troubleshooting revealed that knowledge base retrievals weren't logged, making it impossible to attribute answers to retrieved context. It emphasizes the need for turn-attributable retrieval logging for grounding verification.

The author shares lessons from building customer support multi-agent systems, arguing that retrieval and grounding failures—not prompts or models—are the main cause of agent hallucinations. They outline five grounding checks and note that prohibiting ungrounded answers cut escalations by 40%.

ExtractBench is a new benchmark for schema-guided enterprise document extraction, evaluating value accuracy, record completeness, grounding, and cost across 4,869 pages of enterprise documents. The authors find that commercial VLMs struggle with long documents while coding agents are more accurate but costly, and LlamaExtract AgenticPlus leads on all metrics.

Introduces multi-reference image-grounded video captioning and proposes RefCaptioner, a two-stage post-training framework with mixed-data SFT and hierarchical coverage-discounted GRPO. The paper also presents MRVBench, a benchmark for evaluating caption factuality and multi-reference grounding.

CLBench-V is a benchmark for evaluating multimodal context learning across grounding, new information application, and new knowledge learning. The best model achieves only 0.2847, showing the task remains challenging.

A developer shares how grounding an agent to a knowledge base with retrieval discipline, rather than a better model, solved hallucinations in automated slide generation. The approach splits retrieval from writing and enforces source checking before rendering.

This paper identifies the problem of repetitive copying in long-context reasoning LLMs and proposes GEAR, a reward shaping method that improves grounding by rewarding overlap with key evidence and penalizing irrelevant context copying, achieving consistent improvements of up to +4.6 average points.