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Ternary Bonsai 2 27B is a new multimodal LLM based on Qwen3.8 27B, utilizing ternary weights for a 5.9GB model footprint. It achieves over 9x compression while retaining 98.2% of the full-precision model's aggregate benchmark performance across various tasks. This enables highly capable 27B-class models to run efficiently on local devices.
DeepSeek‑V4.1‑Flash is a 552B‑parameter multimodal Mixture‑of‑Experts LLM that supports up to 1 M‑token contexts while slashing KV‑cache memory to 890 bytes/token (≈¼ of its predecessor) via cross‑layer reuse (CSA2) and FP4 quantisation, plus a SWA‑Bounded Replay scheme that cuts persistent cache to 1/8. The Causal Encoder‑Decoder design halves prefill compute (8B vs 16B active parameters) and th…
This paper introduces a novel evaluation framework to assess the physical world reasoning capabilities of omni-modal generative models like MiniMax-H3. It found that MiniMax-H3 achieved an overall success rate of 41.97% across 517 instances, with significant performance variations depending on the input modalities and reasoning tasks.
OmniVChat defines native audio‑visual dialogue where a model consumes raw audio and video streams and replies in text. The authors build OmniVChat‑Studio, a multi‑agent simulator that generates single‑ and multi‑turn audio‑visual conversations, and use it to create OmniVChat‑Bench, a benchmark covering five dialogue abilities. They also propose OmniVChat‑RL, a reinforcement‑learning reward that b…
HarnessVLN introduces a zero‑shot, training‑free embodied navigation framework that wraps a multimodal LLM in an "Agent Harness" – a tool‑based protocol that validates planner actions against spatial evidence, tracks progress with hierarchical event memory, and maintains a persistent spatiotemporal graph for recovery. The system works for instruction‑following and object‑goal tasks, achieving 60.…
FLAT is a pre‑training framework that encodes images and text into a shared 1D token sequence with variable length via nested dropout, enabling the same embeddings for cross‑modal retrieval and generation. It attains state‑of‑the‑art scores on COCO and Flickr30K for captioning, retrieval, and text‑to‑image generation, and supports interpolation and zero‑shot composed retrieval.
ModAR is a world‑action model that autoregressively denoises multiple future modalities (depth, DINO features, point tracks) before predicting actions, letting each prediction condition on earlier outputs. It outperforms prior WAMs, achieving higher success rates with ~20× fewer training FLOPs and no pretraining.
OmniHarness introduces a symbolic‑policy framework that extracts reusable procedural knowledge from multimodal LLM‑driven visual generation runs. By decoupling task logic from instance inputs, the system can instantiate, adapt, and compose policies for new visual tasks, using intermediate verification for on‑the‑fly refinement while keeping the underlying model frozen. Self‑directed practice task…
The paper proposes the Convergent Emergence Hypothesis that few‑shot in‑context learning (ICL) shares a common difficulty profile across domains. Using a unified task suite, the authors evaluate ICL on six modalities—language, genome, integer sequences, time‑series, images, and proteins—showing that paired‑mapping ICL emerges in all and that per‑task benefits correlate across five modalities, sup…
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