#mllm

5 articles

01 ·Inference & Serving·★ MEMBER·PAPER·8 min read Paper Explained: RewardVerse — A Video Reward Model That Writes the Rubric Before It Scores (RGPO) Video reward models tend to squeeze their scores into a narrow band, and those scores move when the wording changes. The paper calls this scalar drift. RewardVerse first generates an evaluation rubric, then reads a continuous score for each item from token probabilities. Its two-stage RGPO training uses only 30 preference pairs per dimension. 02 ·★ MEMBER·PAPER·11 min read Paper Explained: VDiff-Bench — Even Frontier MLLMs Miss the Low-Level Changes in a Spot-the-Difference Puzzle VDiff-Bench asks a multimodal LLM what changed between two near-identical images, as a four-way multiple choice. We walk through how its 1,756 questions across 10 change categories were built, what 11 models scored, why several of them answer 'no difference' when noise or texture changed, and what this means if you use an MLLM to grade image edits. 03 ·★ MEMBER·PAPER·10 min read Paper Walkthrough — Beyond Retrieval: LatentStream Turns Retrieved Video Into Latent Memory For never-ending video streams, LatentStream stops appending retrieved evidence as extra context and instead internalizes it into fixed-length latent memory tokens. A ground-up walkthrough of its hierarchical memory, latent evolution, and confidence-driven test-time optimization. 04 ·Agents·★ MEMBER·PAPER·10 min read Paper Walkthrough — UrbanGround: Where MLLM Agents Break Down on a Real Street Drop an MLLM agent into a real-scale replica of Hong Kong built from territory-wide 3D geospatial data. Visual recognition clears 90%, orientation sits near 40%, long-range navigation is close to 0%. A walkthrough of the benchmark that measures the gap between seeing and moving. 05 ·Large Language Models·★ MEMBER·PAPER·8 min read Paper Walkthrough: Can Anything Catch a Fake Crisis Video? — What RA-Bench Found Sixteen thousand AI videos, each continuing from the real first frame of a genuine disaster or war clip, put against seven classical detectors, ten zero-shot multimodal models and two purpose-built fine-tunes. None of them generalized. One model turned out to be reading timestamps rather than pixels, and a lap through a social feed drops fake recall to 1.4%.