GRONIK

Build an enterprise-level AI agent operating system enabling cross-departmental and cross-system intelligent collaboration.

[GRONIK @GronikLabs]

✅ Empowers large enterprises with intelligent automation and decision support across business units.
✅ Enables rapid development of AI agents.
✅ Offers visual workflow orchestration and intelligent knowledge-base management.

Gronik is now integrated with SiliconCloud.

@ZainabAyaz_MLE will soon demonstrate how to leverage SiliconCloud’s API services within Gronik.

[GRONIK @GronikLabs]

Total on-chain visibility: 800+ connectors stream EVM, Solana & L2 events for address profiling, whale tracking and AML risk alerts.

DAO governance made easy: proposal summaries, sentiment analytics & auto-voting in one place; Treasury Agents handle multisig scheduling and yield rebalancing.

[GRONIK @GronikLabs]

Smart-contract guardian: AI Audit Agents auto-scan for exploits, generate reports & submit patched PRs—seamlessly plugged into your CI/CD and cutting DeFi bugs by 70%.

[GRONIK @GronikLabs]

Cross-department synergy: Sales, ops & finance agents share insights in real time, auto-triggering multi-party workflows—no more data silos or manual chasing.
Cross-system integration: 800+ built-in API connectors & RPA scripts link ERP/CRM/PLM/IoT, giving legacy systems conversational intelligence in a snap.
Enterprise-grade security: on-prem & hybrid deployment, fine-grained access control, multi-layer encryption & audit logs keep data and compliance protected.

[GRONIK @GronikLabs]

GronikAI acts as the neural hub for enterprises, breaking data silos with real-time cross-departmental AI coordination. Every decision powered by holistic insights!
GronikAI automates workflows across 20+ systems, turning complex processes into seamless automation. Proven 47% efficiency boost in manufacturing!

[Zainab Ayaz @ZainabAyaz_MLE]

AI agents for data analysis are advanced systems designed to process, analyze, and interpret large volumes of data efficiently and effectively.
These agents typically consist of several interconnected components, each playing a crucial role in the data analysis process.

[GRONIK @GronikLabs]

Meet GronikAI - The neural network for your enterprise!

Break down departmental silos, enable cross-system AI agent collaboration, and build true organizational intelligence.

#EnterpriseAI #SmartCollaboration

[Zainab Ayaz @ZainabAyaz_MLE]

Data is the fuel that drives modern businesses. But with so much information at their fingertips, how do businesses make sense of it all? Traditionally, data analysis has been a time-consuming process, often requiring specialized skills and knowledge. This can lead to bottlenecks, slowing down insights and ultimately hindering decision-making.

[Zainab Ayaz @ZainabAyaz_MLE]

Grateful and proud to be a part of the faculty at Army Burn Hall College for Girls, where I recently had the opportunity to contribute as a Resource Person in the Teachers’ Training Workshop 2025.

I conducted a session on "Teaching of Computer Science: Modern Trends, Approaches, Techniques, and Drills" — a topic I hold close as a Computer Science educator. From emerging technologies and AI-driven tools to student-centered pedagogies and engaging drills, the session focused on reimagining CS education to make it more impactful and future-ready.

It was a joy to share knowledge and exchange ideas with fellow educators who are equally passionate about shaping the next generation of tech leaders.

Looking forward to more such learning and growing moments!

[Kunhao Zheng @KunhaoZ]

🚨 Your RL only improves 𝗽𝗮𝘀𝘀@𝟭, not 𝗽𝗮𝘀𝘀@𝗸? 🚨

That’s not a bug — it’s a 𝗳𝗲𝗮𝘁𝘂𝗿𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗼𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲 you’re optimizing.

You get what you optimize for. If you want better pass@k, you need to optimize for pass@k at training time.

🧵 How?

[Sumanth @Sumanth_077]

Stanford released an 1 hour lecture on Agentic AI and is a must-watch for every AI enthusiast!

This 1-hour lecture will teach you everything you need to know to start building with agentic LLMs, including reflection, planning, tool use, and iterative reasoning.

[Akshay 🚀 @akshay_pachaar]

An MCP server to chat with any GitHub repo!

It is powered by GitIngest, and has two tools:
- git_directory_structure → to read the directory structure.
- git_read_important_files → to read files.

100% open-source!

[clem 🤗 @ClementDelangue]

👀👀👀 https://t.co/mekr0Drodq

[Sumanth @Sumanth_077]

Microsoft launched the best course on Generative AI!

The free 21 lesson course is available on Github and will teach you everything you need to know to start building Generative AI applications.

[Rohan Paul @rohanpaul_ai]

Meta released Web-SSL models (DINO ViT, 300Mn to 7B param) trained on 2Bn MetaCLIP images, achieving up to +4.6% higher VQA accuracy over CLIP.

Proves that pure visual self-supervised models can match or beat language-supervised models like CLIP when scaled on web-scale data.

⚙️ The Details

→ Meta trained visual-only DINOv2 ViTs (Web-SSL) from 300M to 7B params on 2B MetaCLIP images. No language supervision was used.

→ On vision-centric VQA, Web-SSL outperforms CLIP by +4.6%. CLIP saturates at 3B params, but SSL shows log-linear scaling up to 7B+.

→ Just 1.3% of data with text (e.g., charts, docs) boosts OCR/Chart accuracy by +13.6%, outperforming CLIP on text-heavy tasks.

→ Resolution scaling to 518px gives further OCR/Chart gains, narrowing the gap with SigLIP.

→ On classic benchmarks (ImageNet, ADE20K), Web-SSL matches or beats DINOv2 and MetaCLIP despite no language labels.

→ All models are integrated with Hugging Face Transformers, fully open-sourced.

[Rohan Paul @rohanpaul_ai]

Incredibly large Math reasoning dataset released by @nvidia

- 540K unique mathematical problems sourced from AoPS forums,

- 3.2M long chain-of-thought (CoT) solutions

- 1.7M long tool-integrated reasoning (TIR) solutions

- 566K samples that select the most promising solution out of many candidates (GenSelect)

[Rohan Paul @rohanpaul_ai]

Existing jailbreak attacks on LLMs often fail to elicit detailed harmful content, leading to poor performance despite high success rates on simple keyword checks.

GeneShift solves this by using a genetic algorithm to automatically find and combine optimal "scenario shifts" (context changes) for malicious prompts, forcing detailed harmful responses while appearing benign, boosting the GPT-based Attack Success Rate (ASR-GPT) from 0% to 60%.

📌 GeneShift uses genetic algorithms to automate crafting effective black-box jailbreak prompts.

📌 It proves simple keyword refusal checks are weak, needing nuanced GPT-based evaluation.

📌 Genetic optimization tailors scenario shifts, creating stealthy prompts bypassing LLM defenses.

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Methods Explored in this Paper 🔧:

→ GeneShift employs a Genetic Algorithm (GA) to automatically explore and optimize combinations of contextual transformations, termed scenario shifts, for jailbreaking prompts.

→ It starts with a population where each candidate prompt is generated by an LLM using a randomly selected subset of transformation rules (genes) from a predefined database.

→ A separate judge LLM assesses the fitness of each candidate prompt based on the harmfulness and detail of the response generated by the target attack model.

→ The GA evolves the population using fitness-proportional selection, crossover (swapping transformation rules), and mutation (randomly replacing or adding rules) to find superior jailbreak prompts.

----------------------------

Paper - arxiv. org/abs/2504.08104v1

Paper Title: "Geneshift: Impact of different scenario shift on Jailbreaking LLM"

[Rohan Paul @rohanpaul_ai]

Group Relative Policy Optimization (GRPO) gives sparse rewards and doesn't promote concise math solutions or handle complex problems well.

GRPO-LEAD enhances GRPO with tailored reward mechanisms, encouraging concise, accurate mathematical reasoning, especially on harder tasks like AIME problems where it improved Pass@1 scores.

📌 Explicit penalties counteract length reward side-effects, balancing brevity (reduced length ~25%) and accuracy.

📌 Difficulty reweighting focuses learning on hard tasks, improving model robustness beyond simple problem optimization.

📌 GRPO-LEAD's shaped rewards provide richer gradients than binary accuracy, accelerating convergence on complex reasoning.

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Methods Explored in this Paper 🔧:

→ A length-dependent reward penalizes long correct answers based on standardized length deviation within a group using `exp(-alpha*z)`. This reduced solution length by up to 26.8% without sacrificing accuracy in ablation studies.

→ Incorrect answers receive an explicit negative reward (-1) to sharpen decision boundaries and discourage guesses, boosting Pass@1 accuracy (e.g., from 0.458 to 0.470 on AIME24 in ablations).

→ Advantage estimates are reweighted using a logistic function based on group correctness, amplifying updates for harder problems and improving performance on challenging validation sets.

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Paper - arxiv. org/abs/2504.09696v1

Paper Title: "GRPO-LEAD: A Difficulty-Aware Reinforcement Learning Approach for Concise Mathematical Reasoning in Language Models"

[Rohan Paul @rohanpaul_ai]

This paper proposes HalluSearch, a system using search-enhanced RAG and factual splitting to find these errors in text across 14 languages.

HalluSearch ranked 4th in English and Czech evaluations, showing competitive performance.

📌 HalluSearch integrates RAG and factual splitting for precise, evidence-backed hallucination localization.

📌 The system's effectiveness heavily relies on GPT-4o for core splitting and verification tasks.

📌 Multilingual performance directly correlates with online data availability for reliable context retrieval.

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Methods Explored in this Paper 🔧:

→ Factual Splitting uses GPT-4o to break the LLM response into distinct, verifiable statements or claims.

→ Context Retrieval employs Google Custom Search, prioritizing Wikipedia results, to gather relevant external information for each claim.

→ Fallback mechanisms, like keyword extraction for refined search or using GPT-4o for context generation, address sparse search results, especially in low-resource languages.

→ Hallucination Verification uses GPT-4o prompts to compare each statement against its retrieved context, identifying specific contradictory substrings.

→ Postprocessing maps these identified hallucinated substrings back to their precise start and end character positions in the original output text.

----------------------------

Paper - arxiv. org/abs/2504.10168v1

Paper Title: "HalluSearch at SemEval-2025 Task 3: A Search-Enhanced RAG Pipeline for Hallucination Detection"

[Rohan Paul @rohanpaul_ai]

GPT-4o's remains uncertain.

This study empirically tests GPT-4o's capability to truly unify image generation with deep semantic understanding, reasoning, and knowledge integration

By evaluating its performance on tasks requiring complex instruction following, precise editing, and logical reasoning during image generation.

📌 GPT-4o's generation defaults to literal text, failing complex reasoning or global instruction overrides.

📌 Editing shows poor localization; GPT-4o alters unintended image regions, lacking fine-grained control.

📌 Post-generation reasoning fails conditional logic, revealing weak state tracking across multimodal steps.

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Methods Explored in this Paper 🔧:

→ The study assesses GPT-4o's adherence to overarching global instructions that modify standard interpretations, like reversing spatial directions.

→ It tests the model's precision in fine-grained image editing, checking if it modifies only intended elements while preserving context.

→ Post-generation reasoning capability is examined by requiring actions based on conditions related to previously generated images.

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Paper - arxiv. org/abs/2504.08003v1

Paper Title: "Have they unified image generation and understanding yet? An empirical study of GPT-4o's image generation ability"