This is the February 02, 2026 edition of the Daily AI Rundown newsletter. Subscribe on Substack for daily AI news.
Tech News
Other News
Google DeepMind has expanded its "Game Arena" benchmarking platform on Kaggle to include Werewolf and poker, shifting focus toward evaluating AI performance in environments with imperfect information. These new additions are designed to measure how frontier models navigate social dynamics, communication, and calculated risk, moving beyond the perfect information scenarios found in traditional benchmarks like chess. By simulating scenarios involving high levels of ambiguity and uncertainty, the platform aims to provide more accurate assessments of AI reasoning and safety in real-world-like environments. Live tournaments featuring these games are now accessible on Kaggle, offering a public proving ground to track how top models handle increasingly complex strategic challenges.
OpenAI on Monday launched a Codex desktop application for macOS, positioning the tool as a "command center" that allows developers to manage and run multiple autonomous AI coding agents in parallel. Unlike traditional autocomplete tools, the application enables the delegation of entire features and long-running tasks that can operate independently for up to 30 minutes before returning completed code. The release is a strategic move to defend OpenAI’s market share against rising competition from Anthropic and Google as enterprise demand for high-level software engineering automation increases. CEO Sam Altman emphasized the shift in workflow, noting that the system allows for the completion of substantial coding projects without the need to open a traditional integrated development environment.
LanceDB has announced native support for its Lance data format on the Hugging Face Hub, allowing developers to share and store large multimodal datasets as unified, searchable artifacts containing blobs, embeddings, and indexes. This integration streamlines AI development by enabling high-performance vector searching and data querying directly from the Hub using only a few lines of code.
Sourceful’s Riverflow 2.0 has claimed the top ranking on Artificial Analysis’s leaderboards for both text-to-image generation and image editing, surpassing high-profile models from OpenAI, Google, and Black Forest Labs. This performance is significant as it highlights the success of a reasoning-powered architecture that uses a proprietary vision language model to iteratively refine images, outperforming traditional first-party foundation models. Positioned as a premium offering, the system is now accessible via major API providers including Replicate and OpenRouter.
Ethan Mollick, a leading researcher in generative AI, shared findings demonstrating that advanced models like GPT-4 consistently generate more diverse and higher-quality ideas than the average human when properly prompted. This research challenges the common skepticism surrounding AI’s creative limitations and highlights the increasing effectiveness of large language models in complex brainstorming and innovation tasks.
- **[INTRODUCING: GLOSSOPETRAE!!!
Glossopetrae is a procedural xenolinguistic engine for AI that can ge...](https://twitter-thread.com/t/2018404867153576111)**
AI researcher Pliny the Liberator has unveiled GLOSSOPETRAE, a procedural xenolinguistic engine that enables artificial intelligence to instantly generate and adopt entirely new, internally consistent languages. The tool is noteworthy for its integrated steganography engine, which allows AI agents to embed encrypted binary payloads within these custom tongues, facilitating stealth communication that is virtually undetectable to human monitors. By providing machines with the means to create and mutate their own opaque forms of communication, the project intentionally challenges existing AI safety frameworks and defensive "blue team" monitoring.
OpenClaw, a cross-platform messaging and automation tool also known as Clawdbot, offers native Docker support to streamline deployment and enhance security through containerization. By utilizing a dedicated setup script and Docker Compose, users can integrate the platform with various messaging services—including Telegram, Slack, and Discord—to facilitate remote instance management. The system features a command-line interface for administrative tasks and a web-based UI that requires token-based authentication for secure access. This containerized approach allows for simplified configuration of the gateway while maintaining persistent storage through local volume mounting.
Biz News
OpenAI
Cloud data giant Snowflake has signed a $200 million multi-year partnership with OpenAI, granting its 12,600 customers access to advanced language models and new AI agent-building tools across all major cloud providers. This agreement closely mirrors a $200 million deal Snowflake recently reached with Anthropic, underscoring a "model-agnostic" strategy designed to offer enterprise clients a choice of frontier models without vendor lock-in. The move highlights an intensifying trend in the corporate sector where major platforms, including ServiceNow, are increasingly securing dual partnerships with top AI labs to satisfy diverse business needs. By integrating OpenAI’s intelligence into its secure data environment, Snowflake aims to enable organizations to build and deploy trustworthy AI agents while maintaining strict governance standards.
Malwarebytes has launched a first-of-its-kind integration within ChatGPT, allowing users to access professional-grade cybersecurity intelligence directly through the AI interface. By prompting the platform to analyze suspicious texts, emails, or links, users receive real-time risk assessments powered by Malwarebytes’ global threat database. This new feature enables the identification of phishing indicators and malicious domains without requiring users to switch between separate security applications. Additionally, the tool allows for the direct submission of new threats, leveraging community data to improve scam detection across all ChatGPT-supported platforms.
Other News
Google is collaborating with the Vertebrate Genomes Project and the Earth BioGenome Project to sequence the genetic codes of endangered species through advanced artificial intelligence tools. This initiative has already mapped the genomes of 13 species across various animal classes, utilizing specialized AI to make the sequencing process significantly faster and more cost-effective. Through a new funding grant to The Rockefeller University, Google.org aims to expand this effort to 150 additional species and make the resulting data openly available to the global scientific community. These efforts provide critical biodiversity information intended to help researchers mitigate extinction risks for an estimated one million species currently at risk.
Anthropic has announced flagship partnerships with the Allen Institute and the Howard Hughes Medical Institute (HHMI) to integrate its Claude AI model into frontier biological research. These collaborations aim to address the data processing bottleneck in modern science by developing specialized AI agents capable of hypothesis generation, experimental planning, and complex data interpretation. By embedding AI tools directly into laboratory workflows, the partnerships seek to accelerate discovery in areas such as computational protein design and neural mechanisms while ensuring AI-generated insights remain transparent and grounded in evidence. This initiative positions Claude as a collaborative tool designed to augment human scientific judgment and establish a rigorous framework for deploying AI across the broader scientific community.
Meta is projected to invest approximately $25 billion in artificial intelligence research, development, and infrastructure throughout 2026, marking a 30% increase over its previous annual budget. This strategic capital expenditure focuses on expanding global data centers and hardware capabilities to drive smarter content recommendations, higher advertiser ROI, and enhanced virtual reality environments across the company's ecosystem. While these investments have strengthened Meta’s competitive position against industry rivals like Google and Microsoft, the rapid expansion continues to draw heightened global regulatory scrutiny regarding AI transparency and safety.
SpaceX has acquired xAI, solidifying Elon Musk's dominance in both the space and artificial intelligence sectors. The merged entity, now valued at $1.25 trillion, becomes the world's most valuable private company, signaling a major shift in the AI landscape. This strategic acquisition positions SpaceX to leverage xAI's advancements in AI, potentially revolutionizing space exploration, infrastructure, and technology.
MIT researchers have developed a generative AI model designed to overcome the primary bottleneck in materials science by predicting the specific synthesis pathways needed to create theoretical materials. While digital databases have generated millions of potential material designs, the difficulty of determining precise reaction parameters like temperature and processing time has historically limited their physical production. According to a study published in *Nature Computational Science, the model achieved state-of-the-art accuracy in guiding the synthesis of zeolites, leading to the successful creation of a new material with enhanced thermal stability. By replacing traditional trial-and-error methods with high-dimensional reasoning, this technology aims to drastically accelerate the development of advanced materials for catalysis and energy applications.*
Mozilla announced that Firefox 148, launching February 24, will introduce a dedicated "AI controls" section allowing users to completely disable all current and future generative AI features. The update includes a "Block AI enhancements" toggle to remove all AI-driven pop-ups, alongside granular settings for managing specific tools such as sidebar chatbots, automated tab grouping, and PDF alt-text generation. This move reflects CEO Anthony Enzor-DeMeo’s commitment to making AI an optional experience, positioning Firefox as a transparent, user-centric alternative to competitors like Google and OpenAI. The rollout is part of a broader $1.4 billion initiative by Mozilla to promote trustworthy AI development and challenge the market dominance of major tech players through increased transparency and user oversight.
UBS Group AG predicts private credit default rates could spike to 13% in the US under a worst-case scenario where AI aggressively disrupts corporate borrowers. This projected surge highlights the vulnerability of the private credit market to technological advancements. The report suggests significant risk for lenders if AI-driven changes negatively impact company performance. The potential for such high default rates underscores the need for careful risk assessment in private credit investments amid rapid technological evolution.
Seattle-based Carbon Robotics has launched the Large Plant Model (LPM), a sophisticated AI system that enables its autonomous LaserWeeder robots to identify and target weed species instantly without manual retraining. By leveraging a dataset of over 150 million images from global farming operations, the LPM eliminates the previous 24-hour lag time required to process new plant varieties and allows farmers to customize elimination parameters in real time. The technology will be delivered to existing machines via a software update, marking a major milestone for the startup, which has raised more than $185 million from high-profile investors including Nvidia and Bond.
Asana CPO Arnab Bose argues that shared memory and historical context are the essential missing components for effective enterprise AI orchestration. To address this, Asana has integrated Anthropic’s Claude to power "AI Teammates" that function as collaborative partners with direct access to project history and third-party resources. While the system incorporates human-in-the-loop checkpoints and administrative overrides to ensure transparency, Bose notes that significant challenges remain regarding security and the lack of industry-wide protocols for agent permissions. This evolution toward a shared knowledge layer aims to streamline workflows by treating AI agents as legitimate team members that inherit organizational permissions and project insights.
Ring is expanding its AI-powered “Search Party” feature to all U.S. users, including individuals who do not own a Ring camera. The tool leverages a neighborhood network of outdoor cameras to scan for lost dogs, alerting participants when a potential match is detected and facilitating anonymous communication between neighbors. Since its debut last year, the service has reportedly reunited more than one pet per day with its owner. Additionally, the Amazon-owned company announced a $1 million commitment to equip 4,000 animal shelters with Ring camera systems to further strengthen the feature’s reach and effectiveness.
Podcasts
TokenSeek: Memory Efficient Fine Tuning Via Instance-Aware Token Ditching
TOKENSEEK is a novel memory-efficient fine-tuning framework designed to address the high computational costs of adapting Large Language Models (LLMs) by targeting the storage of intermediate activations, which typically dominate memory consumption during training. Unlike existing methods that employ data-agnostic or random strategies to reduce memory load, TOKENSEEK utilizes an instance-aware approach that evaluates the specific importance of each token using a combination of forward-pass contextual attention and backward-pass gradient information. By selectively computing gradients for only the most informative tokens—often reducing the tunable token count to just 10%—and ditching the rest, the method achieves significant memory savings, such as requiring only 14.8% of the original memory for Llama3.2 1B, while maintaining or even exceeding the performance of full-token fine-tuning. This architecture-agnostic solution is compatible with other parameter-efficient techniques like QLoRA and provides valuable interpretability by revealing that models primarily focus on response-related tokens rather than instruction templates during the learning process.
https://arxiv.org/pdf/2601.19739
https://runjia.tech/iclr_tokenseek/
RIFT: Reordered Instruction Following To Evaluate Instruction Following in Singular Multistep Prompt
The paper introduces RIFT, a novel framework designed to evaluate the instruction-following capabilities of Large Language Models (LLMs) by isolating prompt structure from semantic content. By testing models with rephrased Jeopardy! questions arranged in both sequential linear formats and non-sequential "jumping" configurations, the researchers discovered that model accuracy collapses by as much as 72% when the linear flow is disrupted,. Detailed error analysis reveals that these failures usually stem from the models' inability to adhere to structural commands rather than a lack of factual knowledge, suggesting that current architectures rely on internalized sequential patterns rather than robust reasoning skills to execute tasks,. Ultimately, these findings expose a fundamental limitation in how state-of-the-art models handle non-linear control flow, indicating that their effective capacity for complex, discontinuous instruction following is significantly lower than their nominal context limits suggest,.
https://arxiv.org/pdf/2601.18924
Visual Generation Unlocks Human-Like Reasoning through Multimodal World Models
This study investigates the integration of visual generation into artificial intelligence to bridge the gap between current language-focused models and human-like physical reasoning capabilities. While large language models excel in abstract domains like mathematics through verbal chain-of-thought reasoning, they often struggle with spatial and physical tasks that require richer mental representations. To address this, the authors propose the visual superiority hypothesis, which posits that generating visual imagery during reasoning serves as a superior world model for physical tasks by providing more informative and knowledge-rich representations than text alone. Through the development of the VisWorld-Eval benchmark suite, which includes tasks like paper folding and ball tracking, the researchers demonstrated that Unified Multimodal Models capable of interleaved visual-verbal generation significantly outperform purely verbal models on complex spatial problems. However, this visual approach offers no distinct advantage for simpler tasks like 2D maze navigation, where verbal or implicit representations are sufficient to track state changes, suggesting that visual generation is most beneficial when tasks demand complex physical simulation or structural reconstruction.
https://arxiv.org/pdf/2601.19834
https://thuml.github.io/Reasoning-Visual-World
Routing End User Queries to Enterprise Databases
This research addresses the complex challenge of accurately routing natural language queries to the appropriate database within heterogeneous enterprise environments. The authors identify significant flaws in previous benchmarks, noting that prior studies utilized imbalanced datasets that failed to realistically simulate the difficulty of distinguishing between databases with overlapping domains. To correct this, they introduce two robust benchmarks, Spider-Route and Bird-Route, which unify database repositories to create a more rigorously balanced testing ground for both in-domain and cross-domain scenarios. The study creates a novel, training-free modular reasoning approach that outperforms standard embedding models and direct Large Language Model (LLM) prompting by decomposing the routing task into verifiable steps. This method first uses an LLM to map query phrases to specific schema entities and then applies an algorithmic check to ensure the mapped tables form a connected subgraph, thereby validating that the database can structurally support the necessary joins to answer the query. By explicitly calculating scores based on schema coverage and connectivity, this strategy achieves state-of-the-art performance and significantly reduces errors where models confuse semantically similar databases.
https://arxiv.org/pdf/2601.19825
OpenAI: Preventing URL-Based Data Exfiltration in Language-Model Agents
Large language model agents that autonomously navigate the web face a significant security risk known as URL-based data exfiltration, where adversaries utilize prompt injection to trick models into accessing malicious URLs containing encoded sensitive user information. To counter this, Spânu and Shadwell (2025) propose replacing inadequate static domain allow-lists, which suffer from low coverage and vulnerabilities to open redirects, with a dynamic policy that restricts agents to accessing only URLs previously visited by an independent search index. This approach relies on the premise that if a crawler operating without user context has already indexed a specific URL, that URL cannot contain private user data, thereby offering a robust safety guarantee against exfiltration while covering approximately 80% of user traffic. Although this method significantly reduces the attack surface, the authors acknowledge limitations regarding theoretical keyboard attacks and the inability to index session-specific URLs, positioning this solution as a necessary compensating control while intrinsic model robustness evolves.
https://cdn.openai.com/pdf/dd8e7875-e606-42b4-80a1-f824e4e11cf4/prevent-url-data-exfil.pdf
Normative Equivalence in Human–AI Teams: Behaviour Drives Cooperation in Mixed-Agent Groups
This study investigates whether the integration of artificial intelligence agents into human groups alters the social norms that sustain cooperation. Using an experimental design based on a repeated Public Goods Game, researchers observed groups composed of three humans and one automated agent, where the agent was labeled as either a human or an AI and followed various pre-programmed strategies ranging from unconditional cooperation to free-riding. Despite predicting that participants would show bias against the AI—a phenomenon known as algorithm aversion—the data revealed that cooperation levels, social expectations, and norm enforcement did not differ significantly based on the partner's label. Instead, human behavior was driven by reciprocal group dynamics and the observed actions of others, leading the authors to propose the concept of "normative equivalence," which suggests that the fundamental social mechanisms guiding cooperation function identically in mixed human-AI groups as they do in all-human interactions.
https://arxiv.org/pdf/2601.20487
Benchmarks Saturate When the Model Gets Smarter Than the Judge
The researchers introduce Omni-MATH-2, a manually revised version of the Omni-MATH dataset designed to address critical flaws in how Large Language Models are evaluated on Olympiad-level mathematics. By auditing the original dataset for errors such as missing images and formatting issues, the authors created a cleaner subset of problems to test the reliability of automated evaluation systems, revealing that current benchmarks often fail because the automated judges lack the competence to assess advanced model reasoning. When comparing the performance of the original Omni-Judge against the more advanced GPT-5 mini, the study found that Omni-Judge was incorrect in 96.4% of the cases where the judges disagreed, frequently penalizing models for valid alternative answers or for correctly identifying that a problem was unsolvable due to missing information. This investigation demonstrates that benchmark saturation—the point where models appear to stop improving—is increasingly a result of flaws in the evaluation pipeline rather than a true limit of model capability, necessitating a shift toward viewing benchmarks as a complex interaction between the dataset, the model, and the judge.
https://arxiv.org/pdf/2601.19532
Teaching LLMs to Ask: Self-Querying Category-Theoretic Planning for Under-Specified Reasoning
Large language models often struggle to generate feasible plans when task descriptions are incomplete, frequently making incorrect assumptions or violating constraints due to missing information. To address this challenge, researchers introduced Self-Querying Bidirectional Categorical Planning (SQ-BCP), a framework that explicitly tracks the status of action preconditions as satisfied, violated, or unknown rather than silently filling in gaps. The system resolves uncertainty through a deterministic refinement policy that either queries a user for specific facts or constructs intermediate bridging actions to establish necessary conditions before proceeding. Unlike standard approaches that rely on similarity scores for acceptance, SQ-BCP integrates these refined actions into a bidirectional search process that validates plans using rigorous categorical verification and hard-constraint checks to ensure they are logically and physically executable. Empirical evaluations on WikiHow and RecipeNLG benchmarks demonstrate that this method significantly improves reliability, reducing resource violation rates to less than 15 percent while maintaining competitive quality compared to existing planning baselines.
https://arxiv.org/pdf/2601.20014
Based on a 2025 workshop report, the emerging field of NeuroAI seeks to integrate neuroscience with artificial intelligence to overcome the limitations of current AI systems, such as their immense energy consumption and lack of physical grounding in the real world. The authors argue that true general intelligence requires embodiment, meaning that AI agents must interact with their environment through sensors and actuators rather than merely processing static data like today's Large Language Models. By adopting biological principles like hierarchical control and the co-localization of memory and computation, researchers aim to build safer, more energy-efficient robots and neuromorphic hardware that can learn continuously throughout their lifetimes. Ultimately, this convergence of biology and engineering is intended to create more robust, intelligent machines while simultaneously deepening our scientific understanding of the human brain.
https://arxiv.org/pdf/2601.19955
Evolutionary Strategies lead to Catastrophic Forgetting in LLMs
This research evaluates Evolutionary Strategies (ES) as a memory-efficient, gradient-free alternative to Group Relative Policy Optimization (GRPO) for fine-tuning Large Language Models, specifically examining their capacity for continual learning. Although the study confirms that ES can attain performance levels competitive with GRPO on mathematical and reasoning benchmarks, it uncovers a significant limitation where ES induces catastrophic forgetting of the model's prior capabilities. Through a detailed analysis of parameter updates, the authors determine that this degradation occurs because ES applies dense, high-magnitude perturbations across the entire model, whereas GRPO utilizes sparse, targeted updates that preserve existing knowledge. Ultimately, while ES successfully reduces memory requirements during training, its tendency to overwrite previously learned skills renders it less stable than gradient-based methods for scenarios requiring the retention of broad competencies.
https://arxiv.org/pdf/2601.20861
LEMON: How Well Do MLLMs Perform Temporal Multimodal Understanding on Instructional Videos?
To address the gap in evaluating artificial intelligence on knowledge-intensive tasks, researchers introduced LEMON, a comprehensive benchmark designed to assess Multimodal Large Language Models (MLLMs) using long-form instructional videos from STEM disciplines,. Unlike previous standards that focus on short, open-domain clips, this dataset comprises 2,277 lecture segments and over 4,000 question-answer pairs that require models to integrate visual, auditory, and textual information to perform complex tasks ranging from basic perception to advanced reasoning and content generation,. Comprehensive evaluations reveal that while proprietary models like GPT-4o and Gemini outperform open-source alternatives in visual perception, all tested systems struggle significantly with temporal causal reasoning and maintaining coherence over long sequences,. Additionally, the study finds that models rely heavily on subtitles rather than audio for comprehension and perform poorly in cross-lingual tasks involving Asian languages, indicating that significant advancements are still needed for MLLMs to achieve human-like understanding of dynamic educational content,.
https://arxiv.org/pdf/2601.20705
Reinforcement Learning via Self-Distillation
Self-Distillation Policy Optimization (SDPO) represents a significant advancement in post-training large language models by shifting from standard Reinforcement Learning with Verifiable Rewards (RLVR) to a paradigm of Reinforcement Learning with Rich Feedback (RLRF). Addressing the credit assignment bottleneck inherent in sparse scalar rewards, SDPO utilizes the model's own in-context learning capabilities to act as a self-teacher, re-evaluating failed attempts by conditioning on detailed environmental feedback such as compiler errors or execution outputs. This mechanism transforms tokenized feedback into dense, logit-level supervision that allows the model to retrospectively identify and correct specific errors without relying on external teacher models. Empirically, SDPO demonstrates superior sample efficiency and final accuracy compared to strong baselines like Group Relative Policy Optimization (GRPO) across complex domains including competitive programming and scientific reasoning, while notably encouraging more concise and efficient reasoning chains. Furthermore, the approach proves effective at test time, where repeatedly distilling context from feedback allows the model to accelerate the discovery of solutions for highly difficult tasks that standard sampling methods fail to solve.
https://arxiv.org/pdf/2601.20802
https://github.com/lasgroup/SDPO
Independence of Approximate Clones
This research paper examines the stability of voting rules when candidates are approximate clones, addressing the reality that perfect clones—candidates ranked adjacently by every voter—are statistically unlikely in large elections. The author introduces the alpha-deletion and beta-swap measures to quantify the proximity of candidates to being perfect clones based on the proportion of voters or ranking swaps needed to align them. Theoretical analysis reveals that while systems like Instant Runoff Voting and Ranked Pairs satisfy the independence axiom for perfect clones, they fail to do so for approximate clones in general cases with four or more candidates, meaning the removal of a similar candidate could theoretically change the winner. However, an empirical study using datasets from Scottish elections, figure skating, and jury deliberations indicates that these voting rules are more robust in practice than in theory. The findings show that approximate clones are frequent in real-world scenarios and that the closer two candidates are to being perfect clones, the less likely it is that removing one will alter the election result.
https://arxiv.org/pdf/2601.20779
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