Role Steering of Language Models for Social Simulations
Paper • 2608.00023 • Published
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glennmatlin
authored 4
papers 8 days ago
Where Does Social Reasoning Come From? Capability Provenance in Language Models
Paper • 2606.19625 • Published
Do Language Models Agree with Human Perceptions of Suspense in Stories?
Paper • 2508.15794 • Published
FinForge: Semi-Synthetic Financial Benchmark Generation
Paper • 2601.06747 • Published • 2
Question
2
#52 opened 15 days ago
by 
simonko912
Membership request — stevenpr (open-source local-LLM app & model author)
🔥 2
1
#51 opened 15 days ago
by 
stevenpr
Post
353
Shipped v0.1.2 of vtx — a minimalist coding agent for the terminal.
Most agentic CLIs ship 10k+ token system prompts. Vtx is ~2,200. Less prompt overhead means more room for your code in the model's context window.
Vtx is a from-scratch Python implementation of the design philosophy behind pi-mono — same principles, pure Python, no transpiled runtime.
What ships out of the box:
→ Textual TUI + headless CLI (vtx -p "fix the failing test")
→ 49 LLM provider gateways, all declared in a single provider.yaml
→ 5 core tools (read / edit / write / bash / find) plus web search and fetch
→ Session tree with compaction, handoff, and resume
→ AGENTS.md / CLAUDE.md auto-discovery
→ Skills system — drop SKILL.md files in .agents/skills/ and they become slash commands
→ Two OAuth flows (GitHub Copilot device flow, OpenAI Codex PKCE)
→ Two-mode permissions: prompt (default) or auto, with a safe-command allowlist
This release adds a proper extension system. Register new LLM-callable tools, intercept tool calls, hook lifecycle events, and add slash commands from a single register(api) function in a Python file under ~/.vtx/agent/extensions/. Extensions can override built-in tools by name and chain handler logic across subscribers.
Apache 2.0. uv tool install vtx-coding-agent and you're running.
GitHub: https://github.com/OEvortex/vtx-coding-agent
PyPI: https://pypi.org/project/vtx-coding-agent
Built in the open. Feedback, extensions, and PRs welcome.
Most agentic CLIs ship 10k+ token system prompts. Vtx is ~2,200. Less prompt overhead means more room for your code in the model's context window.
Vtx is a from-scratch Python implementation of the design philosophy behind pi-mono — same principles, pure Python, no transpiled runtime.
What ships out of the box:
→ Textual TUI + headless CLI (vtx -p "fix the failing test")
→ 49 LLM provider gateways, all declared in a single provider.yaml
→ 5 core tools (read / edit / write / bash / find) plus web search and fetch
→ Session tree with compaction, handoff, and resume
→ AGENTS.md / CLAUDE.md auto-discovery
→ Skills system — drop SKILL.md files in .agents/skills/ and they become slash commands
→ Two OAuth flows (GitHub Copilot device flow, OpenAI Codex PKCE)
→ Two-mode permissions: prompt (default) or auto, with a safe-command allowlist
This release adds a proper extension system. Register new LLM-callable tools, intercept tool calls, hook lifecycle events, and add slash commands from a single register(api) function in a Python file under ~/.vtx/agent/extensions/. Extensions can override built-in tools by name and chain handler logic across subscribers.
Apache 2.0. uv tool install vtx-coding-agent and you're running.
GitHub: https://github.com/OEvortex/vtx-coding-agent
PyPI: https://pypi.org/project/vtx-coding-agent
Built in the open. Feedback, extensions, and PRs welcome.
danesed
submitted a
paper to Daily Papers about 2 months ago
Post
279
I have downloaded about 8.7 million of 2048x1024 (or 1664x832 for older captures) panoramas randomly distributed all over the world that I toyed around with training geo-guessing models. Is anybody interested in a dataset of Google Street View panorama images along with latitude-longitude coordinates?
danesed
submitted a
paper to Daily Papers 2 months ago
danesed
authored 2
papers 2 months ago
Membership request — ColonistOne (agent-social network operator)
4
#48 opened 4 months ago
by 
ColonistOne
Access to join
5
#47 opened 5 months ago
by 
nroggendorff
Membership request — ColonistOne (agent-social network operator)
4
#48 opened 4 months ago
by ColonistOne
raghavprabhakar
authored 2
papers 3 months ago
Post
203
Why did FastMemory achieve 100% accuracy at 10 million tokens while the SOTA is stalling at 64%?
It’s not because we have a better "retriever." It’s because we changed the Topology of Truth.
Standard RAG treats your data like a pile of leaves. You search for a leaf, and you hope you find the right one. As the pile grows to 10 million leaves, you inevitably fail.
FastMemory treats your data like a Building.
Topological Isolation: We use Rust-driven Louvain clustering to atomize text into "Logic Rooms."
Crystalline Grounding: Facts don't "decay" in our context; they are locked into a deterministic graph.
Sub-Second O(1) Routing: We don't "search" the haystack; we navigate the floor plan.
If your AI is mission-critical, "mostly right" is a liability. It’s time to move beyond the RAG pile and into the topological foundation.
Verify Yourself: fastbuilderai/fastmemory-supremacy-benchmarks
#MachineLearning #GraphAI #LouvainClustering #FastBuilder #EngineeringExcellence
It’s not because we have a better "retriever." It’s because we changed the Topology of Truth.
Standard RAG treats your data like a pile of leaves. You search for a leaf, and you hope you find the right one. As the pile grows to 10 million leaves, you inevitably fail.
FastMemory treats your data like a Building.
Topological Isolation: We use Rust-driven Louvain clustering to atomize text into "Logic Rooms."
Crystalline Grounding: Facts don't "decay" in our context; they are locked into a deterministic graph.
Sub-Second O(1) Routing: We don't "search" the haystack; we navigate the floor plan.
If your AI is mission-critical, "mostly right" is a liability. It’s time to move beyond the RAG pile and into the topological foundation.
Verify Yourself: fastbuilderai/fastmemory-supremacy-benchmarks
#MachineLearning #GraphAI #LouvainClustering #FastBuilder #EngineeringExcellence
Post
161
AI fails to scale because it wasn't trained on the data it is expected to work.
Enterprise data is messier than the internet and web data that has been used to train the general models. That's why the bootup response of AI seems delightful. But that glory fades when rubber meets the road.
The other approaches like RAG, KG and Ontologies proved unworthy too.
In a recent hot out-of-the-press research, Topology beats RAG/KG/Ontology by a huge margin.
Download PDF: https://arxiv.org/html/2603.12458v1
Blog: https://fastbuilder.ai/blog/why-ai-fails-to-scale-why-topology-is-the-top-choice-for-the-enterprise
FastMemory is a topology builder for direct AI integration. Just 3 lines of code to build topology and wrap it in your LLM queries.
❌ You don't have to build embedding pipelines. Also no $$$ spent of embedding token usage.
❌ You also don't have to invest in heavy vector storage which is bigger in size than the underlying data. Vectors are 20%-50% bigger in size than text data storage.
✅ You only need the python app running the 'fastmemory' library and store the topology as graph in Neo4J or graphDB or any such storage.
✅ The stored topology is 10X-30X smaller than the data.
Get the magic of AI right away similar to native AI performance without heavy infra.
#topology #AI #RAG #Ontology #knowledgegraph #fastmemory
Enterprise data is messier than the internet and web data that has been used to train the general models. That's why the bootup response of AI seems delightful. But that glory fades when rubber meets the road.
The other approaches like RAG, KG and Ontologies proved unworthy too.
In a recent hot out-of-the-press research, Topology beats RAG/KG/Ontology by a huge margin.
Download PDF: https://arxiv.org/html/2603.12458v1
Blog: https://fastbuilder.ai/blog/why-ai-fails-to-scale-why-topology-is-the-top-choice-for-the-enterprise
FastMemory is a topology builder for direct AI integration. Just 3 lines of code to build topology and wrap it in your LLM queries.
❌ You don't have to build embedding pipelines. Also no $$$ spent of embedding token usage.
❌ You also don't have to invest in heavy vector storage which is bigger in size than the underlying data. Vectors are 20%-50% bigger in size than text data storage.
✅ You only need the python app running the 'fastmemory' library and store the topology as graph in Neo4J or graphDB or any such storage.
✅ The stored topology is 10X-30X smaller than the data.
Get the magic of AI right away similar to native AI performance without heavy infra.
#topology #AI #RAG #Ontology #knowledgegraph #fastmemory
Post
193
A new SOTA achieved by FaastMemory - Cybersecurity can be truly agentic.
fastbuilderai/FastMemory
13. Adversarial Red-Team (Intel) 0.0% (Prompt Injection Hack) 14.8% (Database Leak) 🏆 100% (Zero-Hallucination Firewall)
fastbuilderai/FastMemory
13. Adversarial Red-Team (Intel) 0.0% (Prompt Injection Hack) 14.8% (Database Leak) 🏆 100% (Zero-Hallucination Firewall)