August 21, 2026
SIG-FPT 2026-08-21
Participants: Patrick Nast, Venkatesh Rao, Chris Laidlaw, Robby Rao, Vibhav, durgadas, Ergod, Dan Schmidt, Matthew Bright UTC-7
The first half of the session was a discussion of the ANTS paper, framed by Patrick as a "bridge" between weak-assumption automata models (pebbles/finite-state agents) and richer computational models like ACO. The group explored how evaporation-as-memory changes what stigmergic agents can compute, and floated framings from computer memory (DRAM), information theory, and security. The second half pivoted into planning for the group's late-September **hackathon/workshop**, reviewing a Miro board of proposed game modes for a stigmergy simulator and aligning on an MVP.
- **No reinforcement:** unlike ACO, agents don't distinguish paths by pheromone intensity, so there's no feedback loop toward shortest paths. Agents also **die after a lifetime parameter τ**.
- **Algorithm 1:** essentially tile the grid by dispatching agents north/south to cover untouched tiles; guaranteed to find food within reach of a single agent's span.
- **Algorithm 2:** handles pheromones that evaporate after one step by **specializing roles** — some ants *become* the signal/beacon along paths (constantly refreshing decaying markers), while others explore based on what they read.
- **Chris (first session):** thought of error-correction bits (echoing the DRAM framing); proposed **two pheromones** (data + correction bits) as a low-resource approach. Felt this was a "toy problem" and asked how it connects to a bigger real-world aim.
- **Robby:** read it as breadth-first search with memory in the environment; evaporation becomes a *feature* by reopening paths for re-exploration. Noted correctness argument: any treasure within the ant lifetime τ is guaranteed found. Saw Algorithm 2 as "hacking the algorithm" (turning ants into signal, so μ effectively becomes τ) rather than a new development; wished for reinforcement so signaling ants would emerge naturally.
- **Vibhav:** mapped pheromones onto agents dropping **memories in folders** (in a codebase) with a **TTL** so stale memory can't be read — keeping memory "fresh."
Session Recording Summary · 134m 15s · Full notes ↗
Reading: **"Ants Nearby Treasure Search" (ANTS)** — research associated with computer scientist Amos Korman and experimental biologist Ofer Feinerman. The specific paper discussed extends the ANTS line of work by introducing **evaporating pheromones** (rather than permanent markers). This session is part of an ongoing "summer stigmergy series"; a prior session covered ant colony optimization (ACO) and the De Neubourg work, and earlier sessions covered "Pebble" and Blum/Kozen-style maze-search papers (names approximate from the transcript).
The first half of the session was a discussion of the ANTS paper, framed by Patrick as a "bridge" between weak-assumption automata models (pebbles/finite-state agents) and richer computational models like ACO. The group explored how evaporation-as-memory changes what stigmergic agents can compute, and floated framings from computer memory (DRAM), information theory, and security. The second half pivoted into planning for the group's late-September **hackathon/workshop**, reviewing a Miro board of proposed game modes for a stigmergy simulator and aligning on an MVP.
- **No reinforcement:** unlike ACO, agents don't distinguish paths by pheromone intensity, so there's no feedback loop toward shortest paths. Agents also **die after a lifetime parameter τ**.
- **Algorithm 1:** essentially tile the grid by dispatching agents north/south to cover untouched tiles; guaranteed to find food within reach of a single agent's span.
- **Algorithm 2:** handles pheromones that evaporate after one step by **specializing roles** — some ants *become* the signal/beacon along paths (constantly refreshing decaying markers), while others explore based on what they read.
- **Chris (first session):** thought of error-correction bits (echoing the DRAM framing); proposed **two pheromones** (data + correction bits) as a low-resource approach. Felt this was a "toy problem" and asked how it connects to a bigger real-world aim.
- **Robby:** read it as breadth-first search with memory in the environment; evaporation becomes a *feature* by reopening paths for re-exploration. Noted correctness argument: any treasure within the ant lifetime τ is guaranteed found. Saw Algorithm 2 as "hacking the algorithm" (turning ants into signal, so μ effectively becomes τ) rather than a new development; wished for reinforcement so signaling ants would emerge naturally.
- **Vibhav:** mapped pheromones onto agents dropping **memories in folders** (in a codebase) with a **TTL** so stale memory can't be read — keeping memory "fresh."
Questions & Disagreements: - **Chris's open question:** What is the bigger aim — how does this toy problem apply to a real-world problem? - **Ergod's open theoretical question:** Is there an **information-theoretic floor** on how much agent lifetime you must spend to synthesize persistence out of a memoryless medium? (Motivated by the paper setting μ=1 and requiring τ bounded away from a small constant.) No answer was reach
Participants: Patrick Nast, Venkatesh Rao, Chris Laidlaw, Robby Rao, Vibhav, durgadas, Ergod, Dan Schmidt, Matthew Bright UTC-7