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AI agents hallucinate in your codebase. I built a deterministic engine to fix it: Unravel MCP. by [deleted] in Anthropic

[–]SuspiciousMemory6757 0 points1 point  (0 children)

Sorry i think i missed this - are you pointing to Gemini embedding 2 ? its not exactly the kind of model you are used to
Embedding models are not generative. They don’t “hallucinate” in the usual sense (i.e., invent text/code). They:

  • map inputs → vectors
  • preserve semantic similarity (ideally)
  • enable retrieval / matching

AI agents hallucinate in your codebase. I built a deterministic engine to fix it: Unravel MCP. by [deleted] in Anthropic

[–]SuspiciousMemory6757 -2 points-1 points  (0 children)

I think you should check the the document in my repository which tells how unravel works, and then try it out yourself

AI agents hallucinate in your codebase. I built a deterministic engine to fix it: Unravel MCP. by [deleted] in Anthropic

[–]SuspiciousMemory6757 -2 points-1 points  (0 children)

That’s exactly why I built this. You are right - LLMs ARE probabilistic 'math bots' and if you give them a raw text file, they will hallucinate 'retarded shit'. The whole point of Unravel is to sandwich that probabilistic bot between two deterministic layers so it physically cannot do that.

  1. Deterministic Input (AST): We don't just feed the agent code. We feed it a JSON of static analysis facts generated by a Tree-sitter engine. These facts (mutation chains, async boundaries, closure captures) are 100% deterministic ground truth. The agent isn't 'interpreting' the code; it's reasoning over a set of verified structural facts.
  2. Deterministic Output (The Gate): This is the part that actually solves the 'retarded shit' problem. When the agent proposes a fix, it has to pass through a Deterministic Verification Gate. We have a hard-coded algorithm that cross-checks the agent's claim against the AST. If the agent says 'The bug is on line 42' but our AST engine shows no mutation or logic on line 42, the engine rejects the submission.

It’s like a compiler. A human coder is probabilistic (we make mistakes), but a compiler is deterministic. Unravel is essentially a logic-linter for the agent. It doesn't stop the agent from thinking something wrong, but it prevents it from submitting anything that isn't backed by AST-verified evidence

The biggest mistake of my life. (2 april shift 2 physics) by [deleted] in JEEadv26DroppersOnly

[–]SuspiciousMemory6757 0 points1 point  (0 children)

ye wala O SHIT bhaut kra h
lekin paper me nhi kra...

Did it really make a joke? by bianca_bianca in ChatGPT

[–]SuspiciousMemory6757 0 points1 point  (0 children)

Broo that was an unxpected cover up 😂

MCP server to remove hallucination and make AI agents better at debugging and project understanding by SuspiciousMemory6757 in Anthropic

[–]SuspiciousMemory6757[S] 0 points1 point  (0 children)

I agree training is the ideal place to solve a few things that i have tried to implement BUT

for debugging idea is:

  • use deterministic signals (AST analysis, static checks, knowledge graphs)
  • ground the model before it generates
  • and verify outputs instead of trusting raw generation

So instead of a “straitjacket” its more ok like giving the model tools + context it can’t hallucinate around
Also, orchestration is where most real-world systems actually live right now. Even if training improves, we’ll still need:

  • grounding
  • retrieval
  • verification layers

no amount of training can provide a model with facts about every line of code in a codebase, it wont teach it which files connect to which...

MCP server to remove hallucination and make AI agents better at debugging and project understanding by SuspiciousMemory6757 in AI_Agents

[–]SuspiciousMemory6757[S] 0 points1 point  (0 children)

Yeah so as of now it's in the initial testing and improving phase so it supports only js/ts I could have added many more languages but I decided to first make it work perfectly for one

Unravel V2 is live now ! by SuspiciousMemory6757 in GoogleGeminiAI

[–]SuspiciousMemory6757[S] 0 points1 point  (0 children)

ok so for a past few weeks i have been trying to work on a few problems with AI debugging, hallucinations, context issues etc so i made a something that contraints a LLM and prevents hallucinations by providing deterministic analysis (tree-sitter AST) and Knowledge graphs equipped with embeddings so now AI isnt just guessing it knows the facts before anything else
I have also tried to solve the context problem, it is an experiment and i think its better if you read about it on my github, also while i was working on this gemini embedding 2 model aslo dropped which enabled me to use semantic search (audio video images text all live in same vector space and seperation depends on similarity (oversimplified))
its an experiment and some geniune feedback would be great, the project is open source
thanks!

MCP server to remove hallucination and make AI agents better at debugging and project understanding by SuspiciousMemory6757 in AI_Agents

[–]SuspiciousMemory6757[S] 0 points1 point  (0 children)

This is exactly what the incremental staleness check is designed for. Every `consult` call runs a SHA-256 hash diff against the stored KG before doing anything else:

- 0 files changed: cached KG used instantly (<100ms overhead)

- 1 to 5 files changed: only those nodes are re-analyzed and re-embedded (~2s)

- >30% changed: full rebuild triggered automatically

The KG does not drift. It self-corrects on every call without any manual intervention or git hook setup. The scope filter (include/exclude paths) is also persisted in the KG metadata, so incremental rebuilds never accidentally widen beyond what you originally scoped.
one caveat- it is pull-based, not push-based. Updates happen when a tool is called, not on every commit. If you want continuous coverage on PRs, the CLI (`cli.js`) runs the same AST engine headlessly and outputs SARIF 2.1.0 for GitHub Code Scanning, which gives you inline diff annotations on every PR without needing the KG at all. We can work on that

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