Learning GenAI via SOTA Papers

Source Link: https://prismml.com/news/bonsai-8b Summary: PrismML has announced 1-bit Bonsai, a family of Large Language Models (LLMs) designed to provide high-level intelligence on consumer-grade edge devices. The flagship 8B model features a "true" 1-bit architecture where the entire network—including embeddings, attention, and MLP layers—operates at 1-bit precision. This results in a f...

Paper Link: https://arxiv.org/abs/2603.21687 Summary: The paper "Mirage: The Illusion of Visual Understanding" explores a phenomenon called the mirage effect, where multimodal AI systems generate highly detailed descriptions and reasoning traces for images that were never actually provided. This behavior creates a "false epistemic frame," allowing models to simulate a perceptua...

Paper Link: https://arxiv.org/abs/2603.19461 Summary: The paper "HyperAgents" introduces a framework for self-improving AI systems that can autonomously enhance both their performance on tasks and the very mechanisms they use to improve. Core Innovation: Hyperagents The authors introduce hyperagents, which are self-referential programs that integrate a task agent (to solve problems) and...

Paper Link: https://arxiv.org/abs/2603.16104 Summary: This paper introduces Helium, a workflow-aware serving framework designed to optimize agentic workflows, which are sequences of interdependent Large Language Model (LLM) calls,. The authors argue that existing serving systems are inefficient because they optimize individual inference tasks in isolation and treat LLM calls as black-box functions...

Paper Link: https://qwen.ai/blog?id=qwen3.5 Summary: The paper titled "Qwen3.5: Towards Native Multimodal Agents" introduces the first model in the Qwen3.5 series, Qwen3.5-397B-A17B, which is a native vision-language model designed for high-performance reasoning, coding, and agentic tasks. Built on an innovative hybrid architecture that fuses linear attention (Gated Delta Networks) with...

Paper Link: https://arxiv.org/abs/2603.11088 Summary: This paper presents the first systematic and comprehensive survey of AI agent security, addressing the unique challenges created by hybrid systems that combine large language models (LLMs) with traditional software components. The authors introduce a foundational framework to understand the security landscape, focusing on three primary areas: d...

Paper Link: https://arxiv.org/abs/2603.04241 Summary: Agentics 2.0: Logical Transduction Algebra for Agentic Data Workflows presents a Python-native framework designed to move agentic AI from research prototypes to reliable, enterprise-grade deployments. The paper argues that current "agent-centric" models, which rely on conversational personas and black-box planners, lack the reliabilit...

Paper Link: https://arxiv.org/abs/2603.00873 Summary: MC-SEARCH is a new benchmark designed to evaluate and improve multimodal large language models (MLLMs) as they transition from simple retrieval to complex, agentic reasoning. While older datasets focus on short, single-step tasks, this framework provides 3,333 high-quality examples featuring long reasoning chains that average nearly four hops i...

Paper Link: https://arxiv.org/abs/2602.23720 Summary: The paper introduces the Auton Agentic AI Framework, a principled architecture designed to standardize the creation, execution, and governance of autonomous agent systems. It specifically addresses the "Integration Paradox"—the fundamental mismatch between the stochastic, unstructured outputs of Large Language Models (LLMs) and the de...

Paper Link: https://arxiv.org/abs/2602.15338 Summary: The paper introduces Obj-Disco, an automated framework designed to decompose opaque large language model (LLM) alignment reward signals into sparse, weighted combinations of human-interpretable natural language objectives. The authors address a critical challenge in AI safety: while LLMs are aligned using complex proxy reward functions, these s...

The paper introduces LACONIC (Length-Aware Constrained Policy Optimization), a novel reinforcement learning (RL) framework designed to reduce the verbosity of Large Language Model (LLM) outputs during fine-tuning. While RL-tuning typically enhances reasoning skills, it often leads to excessively long responses that increase inference latency and computational overhead. Unlike previous methods that...

The paper " Language Model Memory and Memory Models for Language " explores the capacity of machine learning models to store input information in hidden layer vector embeddings. The research identifies that standard causal language models typically produce "information-poor" embeddings because the objective of next-token prediction does not require the model to retain arbitrary...

This paper provides a comprehensive survey of the agent skills paradigm , a modular approach that allows large language models (LLMs) to acquire specialized procedural expertise on demand without retraining. Instead of encoding all knowledge in model weights, this architecture uses composable packages of instructions, code, and resources—often formalized through the SKILL.md specification —to enab...

The paper " Improving Code Generation via Small Language Model-as-a-judge " investigates a cost-effective strategy to enhance automated code generation by using Small Language Models (SLMs) —defined as models with fewer than 5 billion parameters—to rival the performance of massive Large Language Models (LLMs). The researchers address the challenge that while massive LLMs are effective fo...

The paper introduces AGENTSYS , a novel framework designed to protect Large Language Model (LLM) agents from indirect prompt injection (IPI) attacks through explicit hierarchical memory management . Conventional LLM agents are vulnerable because they indiscriminately accumulate all tool outputs and reasoning traces in their context window, allowing malicious instructions to persist across multiple...

The paper " Is there 'Secret Sauce' in Large Language Model Development? " (February 2026) investigates whether the rapid progress in Large Language Models (LLMs) is driven by scaling up compute or by proprietary developer techniques. Analyzing data from 809 models released between 2022 and 2025, the researchers decomposed LLM performance into four factors: scaling (compute), sha...

The paper " Large Language Model Reasoning Failures " is a comprehensive survey that systematically categorizes and analyzes the various ways Large Language Models (LLMs) fail at reasoning tasks. To unify fragmented research in the field, the authors introduce a two-axis taxonomy that organizes failures based on the type of reasoning and the nature of the failure. The taxonomy divides re...

The paper, " Agent2Agent Threats in Safety-Critical LLM Assistants: A Human-Centric Taxonomy ," explores the emerging security challenges of integrating Large Language Model (LLM)-based agents into vehicles. As these agents interact with external services via protocols like Google’s Agent-to-Agent (A2A), they create "attack surfaces" where malicious payloads can propagate, pote...

" Uncertainty Quantification in LLM Agents: Foundations, Emerging Challenges, and Opportunities " addresses the critical need for a new framework to measure failure likelihood in large language model (LLM) agents. While traditional research treats LLMs as static oracles for single-turn tasks, this paper argues that uncertainty quantification (UQ) must evolve to handle the multi-turn, int...

The paper " Agentic Proposing: Enhancing Large Language Model Reasoning via Compositional Skill Synthesis " introduces a novel framework designed to overcome the limitations of traditional synthetic data generation for training reasoning models. While existing methods often struggle with logical inconsistency or limited problem complexity, Agentic Proposing treats problem synthesis as a...

The paper " FS-Researcher: Test-Time Scaling for Long-Horizon Research Tasks with File-System-Based Agents " introduces a novel dual-agent framework designed to overcome the context window limitations of large language models (LLMs) during complex, long-horizon research tasks. The core innovation of FS-Researcher is the use of a persistent, file-system-based workspace that serves as an external me...

The paper introduces SERA (Soft-verified Efficient Repository Agents), a new method for training high-performing open-source coding agents at a fraction of the cost of previous approaches. The researchers aim to bridge the gap between closed-source systems and open-weight models by making it practical to specialize agents to private codebases , allowing them to encode repository-specific patterns...

The technical report, " Inference-Time Scaling of Verification: Self-Evolving Deep Research Agents via Test-Time Rubric-Guided Verification ," proposes a new framework called DeepVerifier to enhance the reliability of Deep Research Agents (DRAs). While DRAs are transforming automated knowledge discovery, they remain prone to errors such as hallucinations and incorrect actions. The paper...

The paper introduces MagicGUI-RMS , a multi-agent reward modeling framework designed to create self-evolving graphical user interface (GUI) agents . It addresses the limitations of existing agents—such as their reliance on manual annotations and static rule-based systems—by providing a scalable method for automated trajectory evaluation and feedback. The system's core architecture consists of two...

The paper " Agentic Reasoning for Large Language Models " provides a comprehensive roadmap for reframing Large Language Models (LLMs) as autonomous agents capable of planning, acting, and learning through continual interaction with their environments. This transition marks a shift from static sequence prediction to dynamic, goal-oriented decision-making. The survey organizes agentic reas...