GeoBrowse is a two-level geolocation benchmark combining visual cue composition with knowledge-intensive multi-hop queries, paired with the GATE agent workflow that outperforms no-tool, search-only, and image-only baselines.
Beyond seeing: Evaluating multimodal llms on tool-enabled image perception, transformation, and reasoning
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POW3R adapts rubric criterion weights via rollout contrast in RLVR to improve mean reward, strict completion rates, and training speed over static rubric aggregation on multimodal and text tasks.
WASIL is a released dataset of Arabic spoken interactions with LLMs that includes audio, ASR outputs, responses, user feedback, and answerability labels to isolate ASR effects.
VIGIL is a counterfactual RL alignment method that reduces visual hallucinations in MLLMs by enforcing visual grounding via masked attention penalties, outperforming baselines with 25% of the data and showing emergent spatial capabilities.
Ace-Skill boosts multimodal agent self-evolution via prioritized rollouts with lazy-decay tracking and semantic knowledge clustering, yielding up to 35% relative gains on tool-use benchmarks and zero-shot transfer to smaller models.
citing papers explorer
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GeoBrowse: A Geolocation Benchmark for Agentic Tool Use with Expert-Annotated Reasoning Traces
GeoBrowse is a two-level geolocation benchmark combining visual cue composition with knowledge-intensive multi-hop queries, paired with the GATE agent workflow that outperforms no-tool, search-only, and image-only baselines.
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Not Every Rubric Teaches Equally: Policy-Aware Rubric Rewards for RLVR
POW3R adapts rubric criterion weights via rollout contrast in RLVR to improve mean reward, strict completion rates, and training speed over static rubric aggregation on multimodal and text tasks.
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WASIL: In-the-Wild Arabic Spoken Interactions with LLMs
WASIL is a released dataset of Arabic spoken interactions with LLMs that includes audio, ASR outputs, responses, user feedback, and answerability labels to isolate ASR effects.
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Staying VIGILant: Mitigating Visual Laziness via Counterfactual Visual Alignment in MLLMs
VIGIL is a counterfactual RL alignment method that reduces visual hallucinations in MLLMs by enforcing visual grounding via masked attention penalties, outperforming baselines with 25% of the data and showing emergent spatial capabilities.
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Ace-Skill: Bootstrapping Multimodal Agents with Prioritized and Clustered Evolution
Ace-Skill boosts multimodal agent self-evolution via prioritized rollouts with lazy-decay tracking and semantic knowledge clustering, yielding up to 35% relative gains on tool-use benchmarks and zero-shot transfer to smaller models.