Committee

Nano Banana Pro, also called Gemini 3 Pro Image, is Google DeepMind’s new image generation and editing model built on Gemini 3 Pro. It is positioned as a state of the art system for creating and editing images that must respect structure, world knowledge and text layout, not only style. Nano Banana Pro follows Nano Banana, which was based on Gemini 2.5 Flash Image and focused on fast, casual image editing such as restoring photos and generating figurines. From Gemini 2.5 Flash Image to Gemini 3 Pro Image The earlier Nano Banana model targeted quick creative edits for casual creators. It helped restore old photos and build stylized 3D mini figurines with a simple prompt. Nano Banana Pro keeps that editing flow but runs on top of Gemini 3 Pro, which brings stronger reasoning and real world knowledge into the image stack. The model can turn prototypes, data tables and handwritten notes into diagrams and infographics that reflect the underlying information, rather than producing only decorative art. Reasoning Guided, Search Grounded Visuals A core design point for Nano Banana Pro is reasoning guided generation. Using Gemini 3 Pro, the model can consume text, structured content and references and then plan the image as an explanation of that content. Nano Banana Pro can also connect to Google Search, using the search index as a real time knowledge source. Clear Text and Multilingual Layouts Text inside images is a long standing failure mode for many diffusion based generators. Nano Banana Pro addresses this explicitly. Google states that it is the best model in the Gemini family for producing images with correctly rendered and legible text, for both short taglines and full paragraphs. Gemini 3 Pro’s multilingual reasoning flows into the image model. Nano Banana Pro can render text in multiple languages and also translate text that already appears in products or posters. The documentation shows beverage cans where English text is translated into Korean while the visual design and layout stay unchanged. Studio Level Control, Consistency and Upscaling Nano Banana Pro exposes a set of controls aimed at design and production workflows rather than single shot art prompts. On the composition side, the model can use up to 14 input images and maintain the consistency and resemblance of up to 5 people in one workflow. This supports tasks such as combining reference photos into a single fashion editorial, transforming sketches into product shots or keeping the same cast across multiple scenes. The studio control section of the model page lists several families of controls. Users can vary camera angle and shot type, including wide shot, panoramic and close up, while controlling depth of field and focus on specific subjects in the image. Color and lighting can be adjusted, for example changing day to night, replacing volumetric lighting with bokeh or applying a strong chiaroscuro effect without losing subject identity. Nano Banana Pro supports explicit upscaling. The official Google blog states that it can generate crisp visuals at 1k, 2k or 4k resolution, and provides examples of progressive zoom in operations that keep detail and composition. Aspect ratio is also programmable. Prompts can convert between ratios such as 1:1, 4:3, 16:9 and cinematic formats while keeping the main character locked in place and adjusting only the background. Key Takeaways Nano Banana Pro is Gemini 3 Pro Image, an upgraded image generation and editing model that succeeds Nano Banana, which was based on Gemini 2.5 Flash Image, and is optimized for higher quality and control. The model integrates Gemini 3 Pro reasoning and Google Search grounding so it can turn factual content, documents and real time data into infographics, recipes, process diagrams and other information dense visuals. It provides strong text rendering and multilingual support, producing legible typography in images and enabling translation or localization of existing on image text while preserving layout and design. Nano Banana Pro supports up to 14 input images and maintains resemblance for up to 5 people, with studio style controls for camera angle, depth of field, lighting, aspect ratios and upscaling to 1k, 2k and 4k resolutions. The model is being deployed across Gemini app, AI Mode in Search, NotebookLM, Google Ads, Workspace apps, Gemini API, Google AI Studio, Vertex AI, Antigravity and Flow, with all outputs watermarked using SynthID plus tier specific visible watermarks. Editorial Comments Nano Banana Pro positions Gemini 3 Pro Image as a production oriented image system that links Gemini 3 Pro reasoning, Google Search grounding and structured controls for layout, text and upscaling. It directly addresses long standing issues in text rendering, multilingual localization and subject consistency, while keeping SynthID and visible watermarks as default provenance signals across tiers and surfaces. This launch moves Google’s image stack closer to an integrated, API first visual platform for developers and enterprises. Check out the Technical details. Feel free to check out our GitHub Page for Tutorials, Codes and Notebooks. Also, feel free to follow us on Twitter and don’t forget to join our 100k+ ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well. The post Google DeepMind Introduces Nano Banana Pro: the Gemini 3 Pro Image Model for Text Accurate and Studio Grade Visuals appeared first on MarkTechPost.

arXiv:2403.03628v3 Announce Type: replace Abstract: Topic modeling seems to be almost synonymous with generating lists of top words to represent topics within large text corpora. However, deducing a topic from such list of individual terms can require substantial expertise and experience, making topic modelling less accessible to people unfamiliar with the particularities and pitfalls of top-word interpretation. A topic representation limited to top-words might further fall short of offering a comprehensive and easily accessible characterization of the various aspects, facets and nuances a topic might have. To address these challenges, we introduce GPTopic, a software package that leverages Large Language Models (LLMs) to create dynamic, interactive topic representations. GPTopic provides an intuitive chat interface for users to explore, analyze, and refine topics interactively, making topic modeling more accessible and comprehensive. The corresponding code is available here: https://github.com/ArikReuter/TopicGPT.

arXiv:2504.20039v4 Announce Type: replace Abstract: We introduce AutoJudge, a method that accelerates large language model (LLM) inference with task-specific lossy speculative decoding. Instead of matching the original model output distribution token-by-token, we identify which of the generated tokens affect the downstream quality of the response, relaxing the distribution match guarantee so that the “unimportant” tokens can be generated faster. Our approach relies on a semi-greedy search algorithm to test which of the mismatches between target and draft models should be corrected to preserve quality and which ones may be skipped. We then train a lightweight classifier based on existing LLM embeddings to predict, at inference time, which mismatching tokens can be safely accepted without compromising the final answer quality. We evaluate the effectiveness of AutoJudge with multiple draft/target model pairs on mathematical reasoning and programming benchmarks, achieving significant speedups at the cost of a minor accuracy reduction. Notably, on GSM8k with the Llama 3.1 70B target model, our approach achieves up to $approx2times$ speedup over speculative decoding at the cost of $le 1%$ drop in accuracy. When applied to the LiveCodeBench benchmark, AutoJudge automatically detects programming-specific important tokens, accepting $ge 25$ tokens per speculation cycle at $2%$ drop in Pass@1. Our approach requires no human annotation and is easy to integrate with modern LLM inference frameworks.

arXiv:2511.15862v1 Announce Type: cross Abstract: This paper introduces a novel framework for simulating and analyzing how uncooperative behaviors can destabilize or collapse LLM-based multi-agent systems. Our framework includes two key components: (1) a game theory-based taxonomy of uncooperative agent behaviors, addressing a notable gap in the existing literature; and (2) a structured, multi-stage simulation pipeline that dynamically generates and refines uncooperative behaviors as agents’ states evolve. We evaluate the framework via a collaborative resource management setting, measuring system stability using metrics such as survival time and resource overuse rate. Empirically, our framework achieves 96.7% accuracy in generating realistic uncooperative behaviors, validated by human evaluations. Our results reveal a striking contrast: cooperative agents maintain perfect system stability (100% survival over 12 rounds with 0% resource overuse), while any uncooperative behavior can trigger rapid system collapse within 1 to 7 rounds. These findings demonstrate that uncooperative agents can significantly degrade collective outcomes, highlighting the need for designing more resilient multi-agent systems.

arXiv:2509.26574v3 Announce Type: replace-cross Abstract: While large language models (LLMs) with reasoning capabilities are progressing rapidly on high-school math competitions and coding, can they reason effectively through complex, open-ended challenges found in frontier physics research? And crucially, what kinds of reasoning tasks do physicists want LLMs to assist with? To address these questions, we present the CritPt (Complex Research using Integrated Thinking – Physics Test, pronounced “critical point”), the first benchmark designed to test LLMs on unpublished, research-level reasoning tasks that broadly covers modern physics research areas, including condensed matter, quantum physics, atomic, molecular & optical physics, astrophysics, high energy physics, mathematical physics, statistical physics, nuclear physics, nonlinear dynamics, fluid dynamics and biophysics. CritPt consists of 71 composite research challenges designed to simulate full-scale research projects at the entry level, which are also decomposed to 190 simpler checkpoint tasks for more fine-grained insights. All problems are newly created by 50+ active physics researchers based on their own research. Every problem is hand-curated to admit a guess-resistant and machine-verifiable answer and is evaluated by an automated grading pipeline heavily customized for advanced physics-specific output formats. We find that while current state-of-the-art LLMs show early promise on isolated checkpoints, they remain far from being able to reliably solve full research-scale challenges: the best average accuracy among base models is only 5.7%, achieved by GPT-5 (high), moderately rising to around 10% when equipped with coding tools. Through the realistic yet standardized evaluation offered by CritPt, we highlight a large disconnect between current model capabilities and realistic physics research demands, offering a foundation to guide the development of scientifically grounded AI tools.

arXiv:2511.16397v1 Announce Type: new Abstract: While web data quality is crucial for large language models, most curation efforts focus on filtering and deduplication,treating HTML-to-text extraction as a fixed pre-processing step. Existing web corpora rely on heuristic-based extractors like Trafilatura, which struggle to preserve document structure and frequently corrupt structured elements such as formulas, codes, and tables. We hypothesize that improving extraction quality can be as impactful as aggressive filtering strategies for downstream performance. We introduce MinerU-HTML, a novel extraction pipeline that reformulates content extraction as a sequence labeling problem solved by a 0.6B-parameter language model. Unlike text-density heuristics, MinerU-HTML leverages semantic understanding and employs a two-stage formatting pipeline that explicitly categorizes semantic elements before converting to Markdown. Crucially, its model-based approach is inherently scalable, whereas heuristic methods offer limited improvement pathways. On MainWebBench, our benchmark of 7,887 annotated web pages, MinerU-HTML achieves 81.8% ROUGE-N F1 compared to Trafilatura’s 63.6%, with exceptional structured element preservation (90.9% for code blocks, 94.0% for formulas). Using MinerU-HTML, we construct AICC (AI-ready Common Crawl), a 7.3-trillion token multilingual corpus from two Common Crawl snapshots. In controlled pretraining experiments where AICC and Trafilatura-extracted TfCC undergo identical filtering, models trained on AICC (62B tokens) achieve 50.8% average accuracy across 13 benchmarks, outperforming TfCC by 1.08pp-providing direct evidence that extraction quality significantly impacts model capabilities. AICC also surpasses RefinedWeb and FineWeb on key benchmarks. We publicly release MainWebBench, MinerU-HTML, and AICC, demonstrating that HTML extraction is a critical, often underestimated component of web corpus construction.

arXiv:2502.11866v2 Announce Type: replace Abstract: I describe a new pipeline for extracting wire services (e.g., Associated Press, United Press International, Newspaper Enterprise Association) from newspaper images.

arXiv:2505.14160v4 Announce Type: replace Abstract: Multilingual vision-language models (VLMs) promise universal image-text retrieval, yet their social biases remain underexplored. We perform the first systematic audit of four public multilingual CLIP variants: M-CLIP, NLLB-CLIP, CAPIVARA-CLIP, and the debiased SigLIP-2, covering ten languages that differ in resource availability and morphological gender marking. Using balanced subsets of FairFace and the PATA stereotype suite in a zero-shot setting, we quantify race and gender bias and measure stereotype amplification. Contrary to the intuition that multilinguality mitigates bias, every model exhibits stronger gender skew than its English-only baseline. CAPIVARA-CLIP shows its largest biases precisely in the low-resource languages it targets, while the shared encoder of NLLB-CLIP and SigLIP-2 transfers English gender stereotypes into gender-neutral languages; loosely coupled encoders largely avoid this leakage. Although SigLIP-2 reduces agency and communion skews, it inherits — and in caption-sparse contexts (e.g., Xhosa) amplifies — the English anchor’s crime associations. Highly gendered languages consistently magnify all bias types, yet gender-neutral languages remain vulnerable whenever cross-lingual weight sharing imports foreign stereotypes. Aggregated metrics thus mask language-specific hot spots, underscoring the need for fine-grained, language-aware bias evaluation in future multilingual VLM research.