Benchmarks1y ago
Llama-3.2-11B-Vision-Instruct - Open VLM Leaderboard #165
MMMU_VAL: 48.0 | MathVista: 47.7 | OCRBench: 75.3 | MMBench_TEST_EN_V11: 67.5 | MME: 65.0 | AI2D: 77.3 | MMStar: 49.8 | HallusionBench: 40.3
View sourceMeta
Llama 3.2 11B Vision is a multimodal model with 11 billion parameters, designed to handle tasks combining visual and textual data. It excels in tasks such as image captioning and...
Running this yourself: desktop gpu should be enough.
28.9
Quality Score
---
Arena ELO
11B
Parameters
131K
Context
Sign in to join the discussion
0
Downloads
0
Likes
Sep 2024
Released
Benchmarks
19
Open Source
1
Research
10
Recent launch, pricing, benchmark, and API signals linked to this model or its provider.
MMMU_VAL: 48.0 | MathVista: 47.7 | OCRBench: 75.3 | MMBench_TEST_EN_V11: 67.5 | MME: 65.0 | AI2D: 77.3 | MMStar: 49.8 | HallusionBench: 40.3
View sourceMMMU_VAL: 48.0 | MathVista: 47.7 | OCRBench: 75.3 | MMBench_TEST_EN_V11: 67.5 | MME: 65.0 | AI2D: 77.3 | MMStar: 49.8 | HallusionBench: 40.3
View sourceMMMU_VAL: 48.0 | MathVista: 47.7 | OCRBench: 75.3 | MMBench_TEST_EN_V11: 67.5 | MME: 65.0 | AI2D: 77.3 | MMStar: 49.8 | HallusionBench: 40.3
View sourceMMMU_VAL: 48.0 | MathVista: 47.7 | OCRBench: 75.3 | MMBench_TEST_EN_V11: 67.5 | MME: 65.0 | AI2D: 77.3 | MMStar: 49.8 | HallusionBench: 40.3
View sourceMMMU_VAL: 48.0 | MathVista: 47.7 | OCRBench: 75.3 | MMBench_TEST_EN_V11: 67.5 | MME: 65.0 | AI2D: 77.3 | MMStar: 49.8 | HallusionBench: 40.3
View sourceAs AI-generated text enters the real-world at scale, institutions increasingly use commercial AI-text detectors, especially in education and academic-integrity workflows. We report a surprising empirical finding about such systems: when evaluated by GPTZero and Pangram, generated text from base models is often judged overwhelmingly human, whereas text generated by their instruction-tuned counterparts is not. Building on this observation, we propose Humanization by Iterative Paraphrasing (HIP), a detector-agnostic pipeline that minimally fine-tunes a base model into a paraphraser and applies it iteratively. Compared with the baselines we test, HIP yields a stronger trade-off between semantic preservation and detector evasion on commercial detectors. Across Llama-3 and Qwen-3 families, spanning model sizes from 0.6B to 70B, HIP consistently improves detector human-likeness. Our findings suggest that current detectors are tracking artifacts of instruction tuning and local context more than any invariant notion of machine-generated text. This, in turn, calls for detector designs that model these factors more explicitly.
Modern large language model (LLM) applications increasingly rely on long conditioning prefixes to control model behavior at inference time. While prefix-augmented inference is effective, it incurs two structural limitations: i) the prefix's influence fades as generation proceeds, and ii) attention computation over the prefix scales linearly with its length. Existing approaches either keep the prefix in attention while compressing it, or internalize it into model parameters through gradient-based training. The former still attends to the prefix at inference, while the latter is training-intensive and ill-suited to prefix updates. To address these issues, we propose attention-state memory, a training-free approach that externalizes the prefix into a lightweight, lookup-based memory of precomputed attention states between prefix and query tokens. On ManyICLBench with LLaMA-3.1-8B, our method improves accuracy over in-context learning at 1K-8K memory budgets while reducing attention latency by 1.36x at 8K, and surpasses full-attention RAG performance on NBA benchmark using only 20% of its memory footprint.
Toward recursive self-improvement, we investigate LLM agents autonomously designing foundation models beyond standard Transformers. We introduce a dual-framework approach: AIRA-Compose for high-level architecture search, and AIRA-Design for low-level mechanistic implementation. AIRA-Compose uses 11 agents to explore fundamental computational primitives under a 24-hour budget. Agents evaluate million-parameter candidates, extrapolating top designs to 350M, 1B, and 3B scales. This yields 14 architectures across two families: AIRAformers (Transformer-based) and AIRAhybrids (Transformer-Mamba). Pre-trained at 1B scale, these consistently outperform Llama 3.2 and Composer-found baselines. On downstream tasks, AIRAformer-D and AIRAhybrid-D improve accuracy by 2.4% and 3.8% over Llama 3.2. Furthermore, AIRA-Compose finds models with highly efficient scaling frontiers: AIRAformer-C scales 54% and 71% faster than Llama 3.2 and Composer's best Transformer, while AIRAhybrid-C outscales Nemotron-2 by 23% and Composer's best hybrid by 37%. AIRA-Design tasks 20 agents with writing novel attention mechanisms for long-range dependencies and high-performing training scripts. On the Long Range Arena benchmark, agent-designed architectures reach within 2.3% and 2.6% of human state-of-the-art on document matching and text classification. On the Autoresearch benchmark, Greedy Opus 4.5 achieves 0.968 validation bits-per-byte under a fixed time budget, surpassing the published minimum. Together, these frameworks show AI agents can autonomously discover architectures and algorithmic optimizations matching or surpassing hand-designed baselines. This establishes a powerful paradigm for discovering next-generation foundation models, marking a clear step toward recursive self-improvement.
Extending the context window of large language models typically requires training on sequences at the target length, incurring quadratic memory and computational costs that make long-context adaptation expensive and difficult to reproduce. We propose EndPrompt, a method that achieves effective context extension using only short training sequences. The core insight is that exposing a model to long-range relative positional distances does not require constructing full-length inputs: we preserve the original short context as an intact first segment and append a brief terminal prompt as a second segment, assigning it positional indices near the target context length. This two-segment construction introduces both local and long-range relative distances within a short physical sequence while maintaining the semantic continuity of the training text--a property absent in chunk-based simulation approaches that split contiguous context. We provide a theoretical analysis grounded in Rotary Position Embedding and the Bernstein inequality, showing that position interpolation induces a rigorous smoothness constraint over the attention function, with shared Transformer parameters further suppressing unstable extrapolation to unobserved intermediate distances. Applied to LLaMA-family models extending the context window from 8K to 64K, EndPrompt achieves an average RULER score of 76.03 and the highest average on LongBench, surpassing LCEG (72.24), LongLoRA (72.95), and full-length fine-tuning (69.23) while requiring substantially less computation. These results demonstrate that long-context generalization can be induced from sparse positional supervision, challenging the prevailing assumption that dense long-sequence training is necessary for reliable context-window extension. The code is available at https://github.com/clx1415926/EndPrompt.
Language models are instruction-tuned to refuse harmful requests, but the mechanisms underlying this behavior remain poorly understood. Popular steering methods operate on the residual stream and degrade output coherence at high intervention strengths, limiting their practical use. We introduce contrastive neuron attribution (CNA), which identifies the 0.1% of MLP neurons whose activations most distinguish harmful from benign prompts, requiring only forward passes with no gradients or auxiliary training. In instruct models, ablating the discovered circuit reduces refusal rates by over 50% on a standard jailbreak benchmark while preserving fluency and non-degeneracy across all steering strengths. Applying CNA to matched base and instruct models across Llama and Qwen architectures (from 1B to 72B parameters), we find that base models contain similar late-layer discrimination structures but steering these neurons produces only content shifts, not behavioral change. These results demonstrate that neuron-level intervention enables reliable behavioral steering without the quality tradeoffs of residual-stream methods. More broadly, our findings suggest that alignment fine-tuning transforms pre-existing discrimination structure into a sparse, targetable refusal gate.
In settings where labeled verifiable training data is the binding constraint, each checked example should be allocated carefully. The standard practice is to use this data directly on the model that will be deployed, for example by running GRPO on the deployment student. We argue that this is often an inefficient allocation because it overlooks a reward-density principle: sparse sequence-level reward should train models where exploration is productive, while dense token-level teacher reward should be used where the aim is to compress behavior into a smaller model. In this view, GRPO-style sparse RL and OPD-style dense teacher supervision are not separate recipes; they are different reward-density regimes. The allocation rule is simple: use scarce labeled training data upstream on the strongest model that can turn it into reward-shaped behavior, then transfer that behavior downstream as dense supervision. We evaluate this rule on verifiable math with Qwen3 and Llama models. At fixed Qwen3-1.7B deployment-student size, an RL-improved 8B teacher distilled through the dense bridge outperforms direct GRPO on the same student, while transfer from the same teacher before RL underperforms. The bridge is important: a forward-KL warmup on teacher rollouts followed by OPD on student rollouts is consistently strongest on MATH before any post-bridge student-side sparse RL, and also gives the best pre-Stage~3 AIME endpoints for the canonical 8B/14B teachers. The bridge also makes later student-side sparse RL effective: GRPO that is weak on a cold student lifts MATH from 75.4% to 78.5% after the bridge and outperforms a matched replay control by 2.8 points. The operational principal is to avoid using scarce labeled data on the least prepared policy: use sparse reward for teacher-side discovery, dense transfer for student compression, and student-side sparse reward only after the bridge.
PromptReps showed that an autoregressive language model can be used directly as a retriever by prompting it to generate dense and sparse representations of a query or passage. Extending this to multiple representatives is inefficient for autoregressive models, since tokens must be generated sequentially, and prior multi-token variants did not reliably improve over single-token decoding. We show that the bottleneck is sequential generation, not the multi-token idea itself. DiffRetriever is a representative-token retriever for diffusion language models: it appends K masked positions to the prompt and reads all K in a single bidirectional forward pass. Across in-domain and out-of-domain evaluation, multi-token DiffRetriever substantially improves over single-token on every diffusion backbone we test, while autoregressive multi-token is flat or negative and pays a latency cost that scales with K where diffusion does not. After supervised fine-tuning, DiffRetriever on Dream is the strongest BEIR-7 retriever in our comparison, ahead of PromptReps, the encoder-style DiffEmbed baseline on the same diffusion backbones, and the contrastively fine-tuned single-vector RepLLaMA. A per-query oracle on the frozen base model exceeds contrastive fine-tuning at the same fixed budget, pointing to adaptive budget selection as future work. Code is available at https://github.com/ielab/diffretriever.
Long-context inference in decoder-only language models is costly because long prompts are processed during Prefill, cached at every layer, and repeatedly attended to during autoregressive Decode. We introduce Shallow Prefill, dEEp Decode (SPEED), a phase-asymmetric KV-visibility policy that materializes non-anchor prompt-token KV states only in lower layers while keeping Decode-phase tokens full-depth. Unlike previous approaches that make upper-layer prompt KV states cheaper to store or construct, SPEED removes prefill tokens from the upper-layer Decode visibility set altogether. With a minimal BoS anchor, this simple change preserves broad benchmark quality while reducing long-context cost. In a controlled Llama-3.1-8B instruction-tuning study, SPEED using only 75\% of layers for prefill tokens reaches 51.2 average score on OLMES-style benchmarks, compared with 51.4 for the full-depth baseline, while improving TTFT by 33\%, TPOT by 22\%, and reducing active KV memory by 25.0\% at 128K context. Layer-wise diagnostics suggest that this cutoff retains the main prompt-selection and representation-stabilization regions of the full-depth model. These results show that long-context prompt tokens need not always persist as full-depth KV-cache objects when Decode-phase tokens remain full-depth.
Reliable deployment of language models requires two capabilities that appear distinct but share a common geometric foundation: predicting whether a model will accept targeted behavioral control, and detecting when its internal structure degrades. We show that geometric stability, the consistency of a representation's pairwise distance structure, addresses both. Supervised Shesha variants that measure task-aligned geometric stability predict linear steerability with near-perfect accuracy (ρ= 0.89-0.97) across 35-69 embedding models and three NLP tasks, capturing unique variance beyond class separability (partial ρ= 0.62-0.76). A critical dissociation emerges: unsupervised stability fails entirely for steering on real-world tasks (ρapprox 0.10), revealing that task alignment is essential for controllability prediction. However, unsupervised stability excels at drift detection, measuring nearly 2times greater geometric change than CKA during post-training alignment (up to 5.23times in Llama) while providing earlier warning in 73\% of models and maintaining a 6times lower false alarm rate than Procrustes. Together, supervised and unsupervised stability form complementary diagnostics for the LLM deployment lifecycle: one for pre-deployment controllability assessment, the other for post-deployment monitoring.
Large language models have achieved significant reasoning improvements through reinforcement learning with verifiable rewards (RLVR). Yet as model capabilities grow, constructing high-quality reward signals becomes increasingly difficult, making it essential to understand when RLVR can succeed under weaker forms of supervision. We conduct a systematic empirical study across diverse model families and reasoning domains under three weak supervision settings: scarce data, noisy rewards, and self-supervised proxy rewards. We find that generalization is governed by training reward saturation dynamics: models that generalize exhibit a prolonged pre-saturation phase during which training reward and downstream performance climb together, while models that saturate rapidly memorize rather than learn. We identify reasoning faithfulness, defined as the extent to which intermediate steps logically support the final answer, as the pre-RL property that predicts which regime a model falls into, while output diversity alone is uninformative. Motivated by these findings, we disentangle the contributions of continual pre-training and supervised fine-tuning, finding that SFT on explicit reasoning traces is necessary for generalization under weak supervision, while continual pre-training on domain data amplifies the effect. Applied together to Llama3.2-3B-Base, these interventions enable generalization across all three settings where the base model previously failed.
Llama 3.2 11B Vision Instruct is now available through local Ollama runtime. 8K context window listed. Meta Llama 3: The most capable openly available LLM to date
MMMU_VAL: 48.0 | MathVista: 47.7 | OCRBench: 75.3 | MMBench_TEST_EN_V11: 67.5 | MME: 65.0 | AI2D: 77.3 | MMStar: 49.8 | HallusionBench: 40.3
MMMU_VAL: 48.0 | MathVista: 47.7 | OCRBench: 75.3 | MMBench_TEST_EN_V11: 67.5 | MME: 65.0 | AI2D: 77.3 | MMStar: 49.8 | HallusionBench: 40.3
MMMU_VAL: 48.0 | MathVista: 47.7 | OCRBench: 75.3 | MMBench_TEST_EN_V11: 67.5 | MME: 65.0 | AI2D: 77.3 | MMStar: 49.8 | HallusionBench: 40.3
MMMU_VAL: 48.0 | MathVista: 47.7 | OCRBench: 75.3 | MMBench_TEST_EN_V11: 67.5 | MME: 65.0 | AI2D: 77.3 | MMStar: 49.8 | HallusionBench: 40.3
MMMU_VAL: 48.0 | MathVista: 47.7 | OCRBench: 75.3 | MMBench_TEST_EN_V11: 67.5 | MME: 65.0 | AI2D: 77.3 | MMStar: 49.8 | HallusionBench: 40.3
MMMU_VAL: 48.0 | MathVista: 47.7 | OCRBench: 75.3 | MMBench_TEST_EN_V11: 67.5 | MME: 65.0 | AI2D: 77.3 | MMStar: 49.8 | HallusionBench: 40.3
MMMU_VAL: 48.0 | MathVista: 47.7 | OCRBench: 75.3 | MMBench_TEST_EN_V11: 67.5 | MME: 65.0 | AI2D: 77.3 | MMStar: 49.8 | HallusionBench: 40.3
MMMU_VAL: 48.0 | MathVista: 47.7 | OCRBench: 75.3 | MMBench_TEST_EN_V11: 67.5 | MME: 65.0 | AI2D: 77.3 | MMStar: 49.8 | HallusionBench: 40.3
MMMU_VAL: 48.0 | MathVista: 47.7 | OCRBench: 75.3 | MMBench_TEST_EN_V11: 67.5 | MME: 65.0 | AI2D: 77.3 | MMStar: 49.8 | HallusionBench: 40.3
MMMU_VAL: 48.0 | MathVista: 47.7 | OCRBench: 75.3 | MMBench_TEST_EN_V11: 67.5 | MME: 65.0 | AI2D: 77.3 | MMStar: 49.8 | HallusionBench: 40.3
MMMU_VAL: 48.0 | MathVista: 47.7 | OCRBench: 75.3 | MMBench_TEST_EN_V11: 67.5 | MME: 65.0 | AI2D: 77.3 | MMStar: 49.8 | HallusionBench: 40.3
MMMU_VAL: 48.0 | MathVista: 47.7 | OCRBench: 75.3 | MMBench_TEST_EN_V11: 67.5 | MME: 65.0 | AI2D: 77.3 | MMStar: 49.8 | HallusionBench: 40.3
MMMU_VAL: 48.0 | MathVista: 47.7 | OCRBench: 75.3 | MMBench_TEST_EN_V11: 67.5 | MME: 65.0 | AI2D: 77.3 | MMStar: 49.8 | HallusionBench: 40.3
MMMU_VAL: 48.0 | MathVista: 47.7 | OCRBench: 75.3 | MMBench_TEST_EN_V11: 67.5 | MME: 65.0 | AI2D: 77.3 | MMStar: 49.8 | HallusionBench: 40.3
MMMU_VAL: 48.0 | MathVista: 47.7 | OCRBench: 75.3 | MMBench_TEST_EN_V11: 67.5 | MME: 65.0 | AI2D: 77.3 | MMStar: 49.8 | HallusionBench: 40.3
MMMU_VAL: 48.0 | MathVista: 47.7 | OCRBench: 75.3 | MMBench_TEST_EN_V11: 67.5 | MME: 65.0 | AI2D: 77.3 | MMStar: 49.8 | HallusionBench: 40.3
MMMU_VAL: 48.0 | MathVista: 47.7 | OCRBench: 75.3 | MMBench_TEST_EN_V11: 67.5 | MME: 65.0 | AI2D: 77.3 | MMStar: 49.8 | HallusionBench: 40.3
MMMU_VAL: 48.0 | MathVista: 47.7 | OCRBench: 75.3 | MMBench_TEST_EN_V11: 67.5 | MME: 65.0 | AI2D: 77.3 | MMStar: 49.8 | HallusionBench: 40.3
MMMU_VAL: 48.0 | MathVista: 47.7 | OCRBench: 75.3 | MMBench_TEST_EN_V11: 67.5 | MME: 65.0 | AI2D: 77.3 | MMStar: 49.8 | HallusionBench: 40.3