EXAMPLE.md

# Memento SFT example (partial trace)

Row sampled from `data/memento_sft/partial/val.parquet` by `build_partial_dataset.py`.

The user prompt embeds an **in-progress** reasoning trace shaped like `<think> {cot_truncated}` (no closing `</think>`, no answer). The chain of thought is truncated at a random paragraph boundary so the model is forced to compress what it has produced so far. The assistant target contains only the Memento blocks whose end paragraph falls strictly before the cut; blocks straddling the cut are dropped.

- **data_source:** `microsoft/OpenMementos`
- **split:** `val`
- **domain:** `code`
- **source:** `stackexchange_codegolf`
- **difficulty:** `7`
- **is_partial:** `True`
- **cot_chars (visible):** 38863
- **paragraphs visible / total:** 419 / 601
- **blocks emitted / source total:** 7 / 9
- **truncation_paragraph:** 419

## User message (prompt)

````
You are given a reasoning trace that another model produced while attempting to solve a problem. Your job is to compress the reasoning so that the same model could later replay only the compressed form and continue solving the problem without losing essential context.

The trace below may be **complete** (the `<think>...</think>` block is closed and a final answer follows) or **partial** (the trace ends inside `<think>` with no closing `</think>` because the model is still thinking). Handle either case using the rules below.

## Task

The chain of thought is the text inside the `<think>` region, split into paragraphs by `\n\n`. Number the paragraphs from `0`.

1. Segment a contiguous prefix of the paragraphs (starting at paragraph `0`) into a sequence of reasoning blocks. Blocks must appear in order, must not overlap, and together must cover that prefix exactly.
   - If the trace is **complete**, the prefix is all paragraphs: every paragraph belongs to exactly one block.
   - If the trace is **partial**, stop at the last paragraph that completes a coherent reasoning step. Do not emit a block that ends inside the in-progress trailing material — those trailing paragraphs will continue to grow as the model thinks, so leave them unblocked.
2. For each block, write a concise summary that captures the concrete facts, intermediate results, decisions, equations, candidate answers, and unresolved subgoals introduced in that block. Drop hedges, restarts, second-guessing, and meta-commentary.

Aim for blocks that correspond to coherent reasoning steps (a clarification, a plan, a derivation, a case analysis, a final write-up). A short trace may need only a handful of blocks; a long one may need many. Summaries should be roughly 10-20% of the length of the block they cover.

## Output format

Produce one stanza per block, in order. Within a stanza, the paragraph range and the summary each live on their own line; stanzas are separated by a blank line:

```
<|block_start|>[<start_para>, <end_para>]<|block_end|>
<|summary_start|><summary text><|summary_end|>

<|block_start|>[<start_para>, <end_para>]<|block_end|>
<|summary_start|><summary text><|summary_end|>
```

- `<start_para>` and `<end_para>` are the inclusive 0-indexed paragraph indices into the paragraphs of the chain of thought (the substring after `<think> `).
- The first block starts at `0`; each subsequent block starts at `previous_end + 1`.
- For a complete trace, the last block ends at the index of the final paragraph; for a partial trace, the last block's `<end_para>` may be strictly less than the final paragraph index.
- Do not emit any other text, headers, code fences, or commentary — only the stanzas above.

## Problem

Contest Finished! Read comments on blobs to view their score.
This KoTH is loosely inspired by Primer's Natural Selection Simulation. Your bot is a blob. In order to survive, you must eat pellets to regain energy, which is used to move. With extra energy, blobs can split into two.
Energy and Movement
Your blob starts off each round with 100 energy, and it has no limit on the amount of energy it can collect. Each round is run in turns, with each blob having the option to move North, East, South, or West in any given turn, or stand still. Moving uses 1 energy, and standing still uses 0.25 energy. The map's side length is ceil(0.25 * blobCount) * 2 - 1 units, with a minimum of 9 units. All blobs start on the edge of the map, with one placed in each corner and every subsequent blob being placed 2 units away from any others. Every 30 turns, a wave of pellets are placed in random spots around the map, at least 1 unit from any edge. Each time a wave of pellets appears, the quantity of pellets (originally twice the number of blobs or the width of the map, whichever is larger) in the next wave is decreased by 1, forcing the number of blobs to decrease over time. Each pellet restores between 5 and 15 energy. When a blob's energy is less than or equal to 0, it dies.
Eating
If two or more blobs attempt to occupy the same location, the one with the most energy will eat the others, receiving their energy. If both have equal energy, both vanish.
Detection and Information
Blobs can see any pellets or other blobs within a distance of 4 units. When their functions are called, blobs are provided with:

The side length of the map
The position of the blob on the map
The positions of all pellets within their search radius, as well as their values
The positions of all blobs within their search radius, as well as their energy and UIDs
The energy, UID, and locations of the blob whose function is being executed
A storage object unique to the blob
A storage object shared by all blobs related to the blob through splitting

Splitting
If a blob has more than 50 energy, it can choose to split. Splitting costs 50 energy, and any remaining energy is divided evenly between the two blobs. All blobs are either originals or split copies, with every copy tracing back to an original. All of these together are "relatives." All relatives have one communal storage object. Relatives can still eat each other, and can split, use their own storage object, or collect energy without affecting others.
Energy Transfer
If two blobs are next to each other (after moving), one of the bots can transfer energy to the other. This is done by returning SendNorth(amt), SendEast(amt), SendSouth(amt), or SendWest(amt), with amt being a number representing the amount sent. This can be any amount that the sender can afford, including all of their energy. It is recommended that the blob who is receiving energy is told to stay still through communal storage, so that it does not move away when the energy is being transferred (though the energy would not be deducted from the sender's total in this case).
Functions, Storage, and UIDs
In order to allow more complex learning behaviors, all blobs will be given an integer UID (Unique Identifer). These UIDs will be randomly generated each map, preventing strategies based on individual targets. When a blob's function is called, it is passed four arguments:

The side length of the map as an integer
An object with two arrays: pellets, and blobs. Both arrays contain objects, both having a pos property containing the pellet or blob's position formatted as [x,y]. Pellets will have an energy property, while blobs will have a uid property and an energy property
An object containing various properties of the blob it is passed to: energy, uid, and pos. The pos array is formatted as [x,y]
An object containing the two storage objects of the blob. A self property contains an individual storage object which can be modified however the blob sees fit (by manipulating properties of the object that is passed), and a communal property which can be modified by any relative.

Blobs are not moved immediately to prevent earlier/later turns having an advantage. All movements are processed in groups (All collisions/eating, then all pellets, then splitting, etc.) If a blob lands on a pellet or smaller blob and, in the process uses its last energy, the blob will still consume the pellet/energy independent of whether that would would bring its total energy above 0.
In order for relative blobs to recognize one another, the communal storage must be used for each blob to record its UID in an array, or through some other system.
Return Values
In order to move or split, the return value of the function is used. First, the meaning of the cardinal directions in terms of coordinates:

North = -Y
East = +X
South = +Y
West = -X

Note that [0,0] is the top left corner, and Y increases as you go down. The return value of the function should follow these rules:

To do Nothing: Return nothing, 0, null, undefined, false, or any other value that equates to false
To Move: Return one of four global variables: North, East, South, or West, which equate to "north", "east", "south", or "west" (which could also be used as a return value)
To Split: Return the global variable SplitNorth, SplitEast, SplitSouth, or SplitWest, the direction indicating where to place the new blob

If a split command is returned and the amount of energy required is greater than or equal to the energy of the blob, nothing will happen. Blobs will not be able to leave the map.
Predefined Library Functions
There are a few basic functions available by default, to save some time:
taxiDist(pt1, pt2)
Returns the taxicab distance between two points (X distance plus Y distance).
taxiDist([0, 0], [2, 2]) //4
taxiDist([3, 4], [1, 5]) //3
taxiDist([1.25, 1.3], [1.3, 1.4]) //0.15
taxiDist([0, 0], [5, 2.5], 2.5) //3
taxiDist([0, 0], [2, 4], 2.5) //2.4

hypotDist(pt1, pt2)
Returns distance between two points according to the pythagorean theorem
hypotDist([0, 0], [5, 12]) //13
hypotDist([4, 6], [8, 9]) //5
hypotDist([0, 1], [2, 1]) //2
hypotDist([1, 1], [2, 2]) //sqrt(2)

modDir(dir, amt)
Takes the inputted direction, rotates 90 degrees clockwise amt times, then returns the new value.
modDist(North, 1) //East
modDist(East, 2) //West
modDist(West, 3) //South
modDist(South, 4) //South

Example Blob
This blob will not move until it finds a pellet nearby. Then, it will move in the direction it thinks is most likely to reward it. If its energy is ever above 150, it will split.
function(map, near, me, storage) {
    if (me.energy > 150)
        return SplitNorth;
    if (!near.pellets.length)
        return null;
    var dirs = [0, 0, 0, 0];
    for (let p, i = 0; i < near.pellets.length; i++) {
        p = near.pellets[i];
        dirs[0] += me.pos[1] - p.pos[1];
        dirs[1] += p.pos[0] - me.pos[0];
        dirs[2] += p.pos[1] - me.pos[1];
        dirs[3] += me.pos[0] - p.pos[0];
    }
    return [North, East, South, West][dirs.indexOf(Math.max(...dirs))];
}

Rules

Standard Loopholes are prohibited. Also, no Unstandard Loopholes.
No blob may attempt to modify or read any data not passed to it via its parameters
No blob may attempt to modify a return-value variable to sabotage other blobs
A round lasts until the only remaining blobs are relatives
No blob may modify data by injecting functions into its parameters which modify values using the this keyword
All submissions must either be in Javascript or a language which is not too different from Javascript (Python, for example). All answers will be converted to Javascript for the competition.
The winner is the blob which has collected the highest amount of energy in total across all rounds (from either pellets or consuming smaller blobs that are not relatives)

Controller: https://gist.github.com/Radvylf/1facc0afe24c5dfd3ada8b8a2c493242
Chatroom: https://chat.stackexchange.com/rooms/93370/hungry-blobs-koth


## Reasoning trace

<think> Okay, I need to create a strategy for the blob competition. Let's start by understanding the problem thoroughly.

The blob's main goals are to survive by eating pellets and eventually splitting when it has enough energy. The environment is a grid with other blobs, and competition over pellets and space is key.

First, each blob starts with 100 energy. Moving costs 1 energy each step, staying still costs 0.25. So movement is relatively inefficient. Since moving can use energy quickly, the blob needs to balance being efficient with moving towards resources.

The map size is determined by the number of blobs. The pellets appear every 30 turns, each time decreasing in quantity but the starting number is twice the number of blobs or the map width. So pellets become scarcer over time.

Detection radius is 4 units. So whenever the blob can see pellets or other blobs in that area, it should act. The blob can transfer energy to adjacent blobs, which might be useful for giving energy to relatives to help them survive or grow.

Splitting requires over 50 energy, and costs 50 to split, splitting into two with the remainder split equally. The split directions could be useful to create more blobs, but each needs to manage energy well. Also, all relatives share a communal storage, so maybe they can communicate strategies or track locations.

The UID is important for recognizing relatives. The communal storage can be used to track UIDs so the blob knows when it's dealing with a relative or an enemy. That way, they can avoid attacking their own kind and instead focus on energy transfer and collaboration.

The main problem areas would be:
1. **Movement Strategy**: How to efficiently move towards pellets without expending too much energy. Prioritizing closer pellets first. Also, avoiding other blobs, especially those with higher energy.
2. **Energy Management**: When to split?

Maybe after saving up enough energy but not so much that moving is better. Also, transferring energy to relatives to ensure maximum survival.

3. **Avoiding Conflict**: When another blob is nearby, decide whether to approach or flee based on energy levels. Consuming others could be advantageous, but risking death if energy is low.
4. **Communication via Storage**: The communal storage could be used for things like storing coordinates of known pellets or arranging meeting points for relatives. Since relatives can't attack each other, coordinating them could help gather energy collectively.

First, the example blob moves towards the direction of the most pellets. Maybe my strategy can be more nuanced. Perhaps prioritize moving towards the nearest pellet first, but also account for energy cost of path.

Maybe the blob should:
- When nearby pellets exist, move towards the closest one first. Calculate which direction brings it closer to the nearest pellet each turn.

- When energy is high enough (over 150?), decide to split. Split direction could be chosen to scatter to find new resources, but maybe in a direction away from other blobs.
- Use communal storage to track positions of relatives to form clusters for mutual support. When in a group with relatives, perhaps one splits to extend the family's reach while others transfer energy as needed.
- When encountering another blob, check UID via the data. If it's a relative, ignore (or even send energy).
If it's another blob:
Compare energy. If the blob has less energy, maybe attack. If more, avoid or flee. If same, maybe avoid to prevent mutual destruction.

However, since the fight happens only when two try to occupy the same spot, maybe moving in a direction that avoids collision unless advantageous.

Handling perception radius data: need to process the nearby pellets and blobs efficiently. Maybe prioritize the closest pellets first. Also, for blobs, track if they are relatives.

Energy transfer could be useful for transferring energy from a blob that's about to die to a relative. For instance, if a relative is next to a pellet, but stuck, another relative could send energy to it to help it move.

Potential Steps for the strategy:
1. Check for nearby pellets:    - If any, calculate the closest one, then move towards it, but consider the energy cost. Maybe move towards it only if it's within a certain distance (e.g., move towards it if the taxicab distance is less than some value).

2. Check for nearby blobs:    - For each nearby blob, check if it's a relative (using UID in communal storage).
- If not a relative:      - If their energy is lower than yours, decide to move closer in an attempt to 'win' the fight if you end up in the same spot. So maybe move towards them.
- If their energy is higher, move away to avoid conflict.
- Relatives can be signaled via storage to work in concert, maybe transfer energy if someone is low.
3. Splitting:    - After reaching a split threshold (like 100 energy?), split. But split in a direction where there's less traffic, or where the other blob can have space to collect energy.
4. Use communal storage to log positions of relatives so that they can form groups. Maybe arrange for a 'central hub' where they all gather, then send members out in different directions.

But how to implement this? First code outline:
The function parameters are: map (size), nearby (contains pellets and blobs in view), me (current blob's state), and storage (self and communal).
The storage is passed as an object with self (private) and communal (shared with relatives).

First, in the communal storage, perhaps the blobs can track their own UID and any other relatives so that when they come across a UID they don't recognize (except theirs and stored ones), treat as enemy.

Wait, but communal storage is shared among all relatives. So every time a new blob is split, it can add its UID to the communal store's "relatives" list.

However, since the first blob has to start with no entries, perhaps they should check each initial blob, but maybe that's too much.

Alternatively, when a blob splits, the new one automatically knows every relative's UID via the communal storage. So when a blob is created (split), it can add its UID to communal.relList (if that's existing).

Wait, the split blobs would share the same communal storage as the parent. So when a new blob is created via split, the parent's communal is passed down. Therefore, relatives are naturally tracked automatically.

Wait, no. Because when the parent splits, the new blob would have the same communal storage as the original?

The problem description says: " All of these together are "relatives. " All relatives have one communal storage object. " So yes, any blob that's split from original or any relative shares the same communal storage. Therefore, to find if a nearby blob is a relative, the current blob can check the UID in the communal storage's list.

Wait, perhaps the communal storage should contain a list of all known relatives. So whenever a new blob is split, it adds its own UID to communal.relUIDs (array).
The initial blob (original) would have an empty array?

Wait no. Because when the original is first created (the first in the lineage), the communal storage starts empty. So when it splits, the new child's communal storage is same as parent, so the initial array would not have the original's UID, so when the split happens, the original has to add its own UID.

Wait, perhaps this is a flaw. The original blob starts with a communal storage object, but it hasn't written anything there yet. Therefore, when it splits, the new blob will have that same storage. Thus, the new blob's commune.relUIDs may not exist yet. So when a blob needs to check if another blob is a relative, it may need to see if the UID is in the communal's stored list. Therefore:
Each time a blob initializes (first time in storage), it should check if there is a 'relatives' list in the communal storage. If not, create it and add its own UID.

Wait, but when the first blob (before any splits) does that?
The original blob starts with communal being an empty object. So first run for the original, the function is called with initial storage: {self: {}, communal:{}}.
Therefore, in the function:
Check if 'relatives' exists in communal:
if (!storage.communal.relatives) {     storage.communal.relatives = []; }
Then, if the current blob's UID is not in there, add it:
if (!storage.communal.relatives.includes(me.uid)) {     storage.communal.relatives.push(me.uid); }

This ensures that all relatives are tracked in the communal storage.

So whenever checking a nearby blob, they can look through the relatives array and see if the UID is present.

Now, when dealing with nearby blobs:
for each blob in near.blobs:    blobUID = its uid    if blobUID is in communal.relatives:       it's a relative → no action, perhaps ignore or help    else:       consider as enemy.

Then, when handling enemies:
If the enemy's energy is less than me: maybe try to move towards them to potentially eat them, as that can boost energy. But need to be cautious not to move into a battle where another blob with higher energy also approaches, leading to multiple being in the same place.

Alternatively, targeting an enemy with lower energy requires calculating the direction to them. But if two blobs head towards each other, it might lead to a collision where one (the one with higher energy) takes over. Hmm.

So, maybe the strategy is to prioritize moving towards nearby pellets, but also towards lower energy enemies, but only if it's safe.

Another thought: pellets give energy, but when food is scarce later on, the game shifts to hunting other blobs. So the strategy should change over time. But tracking the current round state would be possible via the communal storage?

Alternatively, perhaps there's no need for that complexity initially.

Now, transferring energy to relatives.

For example, a relative can send energy to another adjacent. To set up for that, one could send a message via communal storage that a certain blob is at position (x,y) and can receive. But maybe it's easier: Nearby relatives can coordinate; when one has excess, they can send to another.

But for the first implementation, perhaps the following steps: Priorities:
1. Stay alive. Don't let energy drop too low (like below 10 energy?  Maybe, but since moving costs 1 per step, having enough energy for several steps is needed. So maybe avoid using energy below 25? Or similar)

2. Prioritize moving to nearby pellets first. The closest one.
- Compute which direction will get closer to the nearest pellet.
- If the pellet is within 1 unit, take it (by stepping into it so the energy is acquired on step, as per movement rules.

Wait, the movement is processed all at once. If a blob moves onto the pellet's position, it will pick it up. So moving to a pellet's square is the way to collect it.

3. If multiple pellets are nearby, choose the highest value first? Or the closest? Because the closest uses less energy to move to it.

Let's think: the energy cost to reach a pellet is distance (taxi?) steps. So each step costs 1 energy. The net gain would be pellet.value (5-15) minus energy used to reach it.

For example, a 5-value pellet 1 away: net gain 4.

An 15-value 3 away: gain is 12. So that's better.

So the per-pellet score could be (value / distance) or value - distance. Depending on what's better.

But distance here is Manhattan distance?

So for each nearby pellet, compute value/(distance + 1) perhaps, or value/ (distance^2), to prioritize closer positions.

Alternatively, the net profit: value minus distance.

If the net is positive, then it's worth doing.

Thus, target the pellet with the highest (value/distance) ratio. Or highest (value - distance * cost_coeff). Since moving costs 1 per distance unit.
Alternatively, the optimal pellet would be the one which gives the maximum (value - distance), so that over time, optimal resource.
Hmm, but also, if the distance is zero (already on it?), then taking it. Wait, no, the pellet is at position, so when the blob moves there, it collects.

So the code can:
Compute all nearby pellets, then for each, calculate distance from me.pos to pellet.pos. Then calculate value - distance as a metric.

Choose the pellet with highest metric. If multiple, pick the one closest.
If multiple have the same metric, choose closest. Because closest uses less energy.

Wait, but if two pellets both have the same (V-D) but one is closer, same stats, then pick the closer one.
So the selection process can sort first by highest (V-D), then by distance ascending.

Once the best pellet is selected, the direction to move towards it.

Calcuclate direction:
current position is [x,y]
pellet at [px,py]

x_diff = px - x. If positive, the direction needed in X direction is East (add 1) or no? Wait, according to the directions:
The coordinates are [x,y], where North is -Y direction. So moving East increases x by 1?

Wait the movement directions are:
- North: subtract from Y. So if the blob's Y is 5 and moving North to Y4.

The coordinate is [X,Y]. North reduces Y.
East is +X. South is +Y. West subtracts X.

So the direction to the pellet:
Let me see, the displacement from me's position to the pellet.

Δx = px - x
Δy = py - y
Want to move in the direction that minimizes the distance (taxi). So:
If DeltaX positive: move East if possible, otherwise West. Similarly for Y.

Wait, moving North (down Y) would reduce the X distance if that's what's needed? Hmm, need to calculate step direction.
The movement direction can be a unit vector. For example, to get closer to (px, py):
if Δx > 0: need to go East (x+1)
if Δx <0: West (x-1)
Δy >0: South (y+1)
Δy <0: North (y-1)

So the direction is the primary direction with the largest Δ?

Alternatively, prefer to move in the direction where both deltas would be adjusted first.

Wait, for a pellet at (x=10, y=10), when I'm at (x=5, y=8):
Δx =5, so east.
Δy = +2 → South.

But since moving either direction reduces distance, but takes different paths, perhaps looking for the direction that reduces the larger delta first.

Alternatively, pick the direction of higher delta? Not sure.

Alternatively, pick one direction towards the pellet. For example, moving in X if larger absolute delta in X, otherwise Y.

Alternatively, just pick a direction that can move one step towards the pellet.

The movement's direction must be North, East, South, or West, so for example, if the pellet is to the North (Δy negative), you need to pick North as the direction.

So, the first step is to find the closest pellet with the best value, then choose a direction that's towards it. Another algorithm:
Let me think of the direction selection.

For example, the current position is (x,y). The pellet is at (px, py). Possible movement direction:
If I can move to decrease distance towards the target:
For X direction: if px > x → go East (East). If px <x → West.
For Y direction: if py < y → North (since moving North reduces Y). If py > y, South.

So the blob can move in a direction that reduces either the X or Y component of the delta.

Thus, the movement direction is the direction best aligned with the pellet's position.

But since movement can only go in one direction per step, the choice could be between the X or Y direction to prioritize.

Possibly choose the direction axis (X or Y) with the larger delta (in absolute terms)?

Alternatively, movement could prioritize decreasing the largest delta first.

Alternatively, the distance in X and Y, the direction is chosen to cover the larger? For example, if the pellet is 2 steps east (Δx 2) and 0.5 steps north, then move east.

Alternatively, which direction reduces the distance more? Each movement in the right x or y direction would reduce the distance (taxi) by one unit. So any direction taken correctly will reduce distance by one.

Thus, direction is picked as any direction which reduces either X or Y Delta.

The code can pick direction towards the pellet based on the direction where we can make progress in either direction.

So, for the directions, can choose one:
perhaps randomly, but preferring the larger delta?

E.g. first check whether X delta is larger than Y delta. In that case, move towards X direction. Else, move in Y direction.

Alternatively, just rotate through directions based on the delta's sign.

Thus, the code can compute Δx and Δy. Then direction choice is:
for the x direction, whichever direction reduces the x difference. Similarly for y.

But since the step can choose between East, West, South, North, pick one that's in a direction that moves towards the pellet.

So for example, if Δx is positive (target is to the East), then choose East as the possible direction.
If Δx is zero but Δy is negative (target North), choose North.
If both directions are possible (Δx and ΔY are both non-zero)), then pick one direction (could pick randomly, or choose a bias, like move in X if the x delta is bigger in absolute terms, else Y. So in code:
function select direction towards (px,py):
var dx = px - me_pos[0]
var dy = py - me_pos[1] var directions = []
if (dx >0) directions.push(East)
else if (dx <0) directions.push(West)
if (dy <0) directions.push(North)
else if (dy>0) directions.push(South)
// pick one of the possible directions (directions array has elements)
// ... choose one, perhaps the one with larger delta?

if (dx && dy) {
if (Math.abs(dx) >= Math.abs(dy)) {
return directions[0] (the X direction) } else {
return directions[1] (the Y direction) } } else if (directions.length) {
return directions[0] }

But this will fail if both are equal. Alternatively, when both are possible, randomly choose?

Alternatively, pick the first possible direction.

But deterministic is better for consistent turns.

Alternatively, use the larger absolute delta.

Hmm. Well, in code, the exact direction can be programmed as above, perhaps.

Now, another consideration: after prioritizing moving towards the pellet, the blob must also avoid moving into a spot that collides with a higher energy blob.

For instance, if moving towards the pellet takes the blob into the same cell as a higher-energy non-relative, then it will lose. So this must be avoided.

Thus, the next step: check nearby blobs, and plan movements so that they don't move into a location with a non-relative that has higher energy than our blob.

Alternatively, compute all possible movement directions, and choose those where the destination doesn't lead to collision with a dangerous blob.

So, before moving towards the pellet, the blob should check:
for every possible direction in the movement candidates (the directions towards the pellet):
compute destination position (x,y) when moving in that direction (using the direction's coordinates change)
Then check, if moving to that destination, are there any nearby (within radius? Or specifically in that cell?) other blobs.

Wait, collisions are immediate: if two blobs try to occupy the same cell, the higher-energy one eats the rest.

Thus, the destination cell's current occupants are the ones in that direction.

Wait, the current data "near" includes all blobs within 4 units of the current position. So, the target destination cell (distance 1) would likely be within the 4-unit radius, so the blob can see them in near.blobs.

But in code, when choosing to move in a direction, the destination is one cell away.

So for each candidate direction (towards the pellet), compute where that move would result in the new position, then check all nearby blobs to see:
- Is there any blob in the blob's new position?

- If yes, and it's a non-relative with higher energy, then moving there would result in death.

So the algorithm can:
First, precompute all possible directions towards the pellet (x and/or y direction).
Then, for each direction in that list (like East in the example):
newpos = destination when moving in that direction.

Check if any blobs in near.blobs are in newpos:
For each blob in near.blobs:
if blob.pos is equal to newpos:
then, that blob's energy vs ours?
if it is a non-relative (not in communal relatives):
if its energy >= my energy: moving here is bad. (unless my energy + any gain from eating, but the blob has more, we lose.

so: if it's a not relative and energy higher than ours, avoid this direction.

Also, check pellets in newpos? But a pellet in newpos will be taken, but as long as that position has the pellet, the blob can move there normally. Wait, no, pellets are treated differently than blobs. Blobs take priority in collisions. If there's a blob there, the two collide.

Thus, the code needs to evaluate each candidate direction, and for each:
Simulate moving in that direction to newpos. Check:
Any blob in that newpos?
If so, check:
Is that blob a relative? No → okay? Or need to check.
If it's a non-relative:
if their energy > mine: moving here is bad (we lose)
if their energy < mine: moving here could result in me eating them (so that's good?)
if equal energy: both die.

So, moving into their cell can be beneficial if their energy is less than mine. But only if it's allowed (and we have enough energy to do so, since movement costs energy).

But maybe, the blob can calculate which direction is safe and advantageous. So among the directions towards the pellet, check each of their destination positions for such. So:
1. Compute all possible directions towards the closest pellet.

2. For each direction from this list:
a. calculate new position after moving in that direction.

b. check if any non-relative blobs are present at newpos:
i. for each such blob, if blob's energy > my energy, then moving there loses.
ii. if any such blob exists with higher energy → direction is bad.
c. if any bad blobs (non-relatives with higher energy in the newpos): remove this direction.

3. if after filtering, there are any acceptable directions, choose the first, or highest-scored?

Alternatively, select from the acceptable directions the one which closest to the pellet (the initial selection criteria), and pick that.

If none of the directions are safe, then either choose to stay still, or pick another direction that doesn't lead to danger, even not moving towards the pellet.

This adds another layer of evaluation before moving towards a pellet.

Hmm, this requires a lot of processing.

Alternatively, the direction selection can factor in the safety (no non-relatives with higher energy in the target cell).

Alternatively, if there are possible directions to move towards the pellet that are safe, pick those. Otherwise, maybe retreat.

Alternatively, the process could be:
First, find all nearby non-relative blobs.

For each, if they are in my path towards the pellet? Or anywhere that my movement might collide.

Alternatively, in the direction choices, we must avoid hitting any blobs with higher energy than me.

Therefore, when considering each direction, if any non-relative in the target cell with higher energy, don't choose that.
If after all possible directions towards the pellet are blocked by dangerous blobs, then perhaps choose to stand still to conserve energy, or another direction (possibly away).
This is getting complex. Perhaps in code, the steps can be as follows:
First, collect all nearby non-relative blobs: var threats = [];
for each blob_entry in near.blobs:
var iblob = blob_entry;
if not ( Uncomment? Wait, check: is this a relative?
if (communal.relatives.includes(iblob.uid)){
// relative → safe, ignore } else {
threats.push(iblob); }

Then, for each of these threats:
evaluate if their distance direction can put us into danger.

The threats could be anywhere, but collision only occurs if we end up moving to their position.

Alternatively, the main threats are those in a current distance such that moving towards the pellet would cross into their position.

This is hard to track.

Alternatively, the blob's decision process can be:
Check all possible directions (up, down, left, right, or the possible directions towards the desired pellet)

For each such possible direction (maybe only the pellet approach directions, or all possible directions as a general move options?):
Simulate moving in this direction. Compute new position. Check:
a) where am I moving to:
- if that cell has a non-relative blob (check all near.blobs):
for each such blob:
if blob.pos == newpos:
// that's a threat here.
if blob_energy > our energy:
this direction is dangerous.

Also, if the cell has a pellet, then good.

But note that other blobs may also be moving in the next turn, but since all movements are processed simultaneously, the blob's movement is based on current information. So the decision is made on current data.

Thus, the algorithm could proceed step by step:
1. Determine the closest, best pellet.

2. Determine the direction to move towards the pellet (East if dx>0, etc)
3. When a direction is picked (among towards pellet's directions), check whether that new position has a non-relative with higher energy.
4. If so, avoid that direction.
5. If such movement is safe, proceed.
6. Else, consider other directions towards other pellets.

Wait, perhaps need to loop over all possible pellet targets first, and choose the optimal direction among the safest.

This may get too complex for initial code. Perhaps a simplified approach is needed. Maybe in code:
First, calculate the best pellet target based on value/distance ratio or some metric.

Then, compute direction as best towards that target. Then check that direction for collision with dangerous blobs.
If safe, move in that direction.
If not safe, try other possible directions towards the pellet (like orthogonal? Not sure).

OR maybe move in some random safe direction.

Alternatively, give up, and stand still to conserve energy.

Another thing: the blob should also look for other blobs' energy to attack (if they are lower ener יצא than ours).

Perhaps moving towards lower-energy non-relatives is also a good strategy to gain their energy.

But then again, moving there can lead to other blobs also moving there, causing conflicts.

Alternatively, if a nearby non-relative has energy lower than mine, and is within movement direction, we can choose to move towards them to attempt to eat them.

But this requires managing distance. Hmm.

Perhaps the priority order for the blob's actions should be:
Priority 1: Avoid death (moving into a spot with a stronger enemy)

Priority 2: Seek to find a pellet or neutral blob to eat.

Priority 3: Split when energy is sufficient.

Implementation steps for code: First code structure:
function(map, near, me, storage) {

```python
// update communal storage with UID (if not already present)

if (!storage.communal.relatives) {
    storage.communal.relatives = [];
}

if (!storage.communal.relatives.includes(me.uid)) {
    storage.communal.relatives.push(me.uid);
}

// Compute all relevant blobs (attacking/defending)

// Compute nearby threats (non-relatives)

var threats = [];

for (let b of near.blobs) {
    if (!storage.communal.relatives.includes(b.uid)) {
        threats.push(b);
    }
}

// Determine best pellet, etc.

// Find pellets in near.pellets and select optimal one.

let best_pellet = null;

let max_metric = -Infinity; // value - distance

for (let p of near.pellets) {

    let dx = p.pos[0] - me.pos[0];

    let dy = p.pos[1] - me.pos[1];

    let distance = Math.abs(dx) + Math.abs(dy); // taxi distance.

    if (distance ==0) { 

        // already here, can consume pellet → should end up here? But to collect it, need to stay?

        // but movement to the spot would collect it.

        // but perhaps just do nothing, since already there?

        }

    let value = (p && p['energy']) ? p.energy :0; // assuming p has energy.

    let metric = value - distance;

    if (metric > max_metric || (metric == max_metric && distance < best_pellet.distance)) {

        best_pellet = { pos: p.pos, value: value, distance: distance };

        max_metric = metric;

    }

}

// after that, decide direction.

let dir = null;

if (best_pellet) {

    // compute direction towards the best pellet.

    let target = best_pellet.pos;

    let dx = target[0] - me.pos[0];

    let dy = target[1] - me.pos[1];

    let possible_dirs = [];

    if (dx > 0) {

        possible_dirs.push(['East', 1, 0]); // East is +x.

    } else if (dx <0) {

        possible_dirs.push(['West', -1, 0]); // West is -x.

    }

    if (dy > 0) {

        possible_dirs.push(['South', 0, 1]); // South increases Y?

    } else if (dy <0) {

        possible_dirs.push(['North', 0, -1]); // North reduces Y.

    }

    // pick direction based on the bigger delta.

    if (dx && dy) {

        if (Math.abs(dx) > Math.abs(dy)) {

            // pick first (dx direction)

            dir_str = possible_dirs[0][0];

        } else {

            dir_str = possible_dirs[1][0];

        }

    } else if (possible_dirs.length >0) {

        dir_str = possible_dirs[0][0];

    }

    // now compute new position for that direction:

    let move_dir = possible_dirs[0][0]; // wait perhaps not. Need to code better.

    let new_pos = compute the new position based on the direction chosen.

    // Wait, this computation is confusing. Alternatively, pick one direction (the first in possible_dirs).

    // wait, perhaps need to code directions properly.

    let move_dirs = [];

    for (let d of possible_dirs) {

        move_dirs.push(d[0]);

    }

    if (move_dirs.length ==0) {

        // we are already at the pellet's position. So if so, do nothing. (Take the pellet?)

        // but movement would cancel it? Wait no: when you are already on it, then since when we move, we don't move (return null?), then the pellet is collected.

        //Wait, if best_pellet's distance is 0, then the target is the current position. Thus, no need to move.

        // So, for that case, when we have best_pellet.distance ==0 → return null.

        if (best_pellet.distance ==0) {

            // so move would be standing still → return null.

            // but do nothing? Because staying and processing it?

            dir = null;

            break;

        }

    }

    let chosen_dir;

    if (possible_dirs.length ==0) {

        // no directions needed?

        // this can happen if dx=0 and dy=0 → already at the pellet. So no move.

        dir = null;

    }

    else {

        // pick direction as per earlier logic.

        // For code's sake, just pick the first direction if multiple.

        // Or choose the one that is in the direction of larger delta.

        // perhaps, if both dx and dy are non-zero, choose whichever has larger delta.

        let dx_abs = Math.abs(dx);

        let dy_abs = Math.abs(dy);

        chosen_dir = possible_dirs[0][0]; // default pick East or West.

        if (Math.abs(dx) == Math.abs(dy)) { // equal: pick any?

            chosen_dir = possible_dirs[0][0];

        } else {

            if (dx_abs > dy_abs) {

                chosen_dir = possible_dirs[0][0]; // dx's direction.

            } else {

                chosen_dir = possible_dirs[possible_dirs.length ==1 ? 0 : 1][0]; // the dy's direction.

            }

        }

    }

    // now check chosen direction's validity:

    // compute new pos:

    let direction = toDirection(chosen_dir); // converts East → +x, etc.

    // function toDirection(dir_str) returns [dx, dy]

    // helper function inside the blob?

    function direction2_change(direction) {

        if (direction == 'North') return [0, -1];

        if (direction == 'South') return [0, +1];

        if (direction == 'East') return [+1, 0];

        if (direction == 'West') return [-1, 0];

        return [0,0]; // invalid?

    }

    let move_vector = direction2_change(chosen_dir);

    let new_x = me.pos[0] + move_vector[0];

    let new_y = me.pos[1] + move_vector[1];

    let newpos = [new_x, new_y];

    // Now check this new position for threats.

    let danger = false;

    for (let t of threats) {

        let t_pos = t.pos;

        if (t_pos[0] == new_x && t_pos[1] == new_y) {

            // If moving here would lead to collision with this threat.

            // their energy ≥ me → danger.

            if (t.energy >= me.energy) {

                danger = true;

            }

        }

    }

    // Also check if there are other spawns here? Not necessary, since by my calculation?

    if (danger) {

        // can't move there.

        // Try to find another direction among options that can lead there without collision?

        // alternative approach: pick other possible directions from possible_dirs list, until a safe one is found.

        for (let d_entry of possible_dirs) {

            let trial_dir_str = d_entry[0];

            let trial_vector = direction2_change(trial_dir_str);

            let trial_new_x = me.pos[0] + trial_vector[0];

            let trial_new_y = me.pos[1] + trial_vector[1];

            let trial_pos = [trial_new_x, trial_new_y];

            // check dangers for this trial direction:

            let trial_danger = false;

            for (let t of threats) {

                if (trial_pos[0] == t.pos[0] && trial_pos[1] == t.pos[1] ) {

                    if (t.energy >= me.energy) {

                        trial_danger = true;

                        break;

                    }

                }

            }

            // if no danger, then pick this direction.

            if (!trial_danger) {

                chosen_dir = trial_dir_str;

                new_x = trial_new_x;

                new_y = trial_new_y;

                dir = trial_dir_str;

                danger = false;

                break;

            }

        }

        if (danger) {

            // All directions around the pellet's path have danger → cannot proceed there.

            best_pellet = null; // abandon this pellet and seek next option.

            // reset to reevaluate.

        }

    }

    // if after this check, direction is safe or can get it.

    if (danger) {

        // no direction safe → not proceed towards this pellet.

        best_pellet = null;

        dir = null;

    } else {

        dir = chosen_dir;

    }

}

// Now, after pellet handling, if no dir selected, look for other options.

// Maybe attack a low energy blob.

// look for non-relative blobs with energy < me.energy?

let target_blob = null;

let closest_dist = Infinity;

// We need to find a blob (non-relative) we can attack.

for (let t of threats) {

    let t_energy = t.energy;

    if (t_energy < me.energy) {

        let dx = t.pos[0] - me.pos[0];

        let dy = t.pos[1] - me.pos[1];

        let dist = Math.abs(dx) + Math.abs(dy);

        if (dist < closest_dist) {

            // closest and can be eaten.

            closest_dist = dist;

            target_blob = t;

        }

    } 

}

if (target_blob) {

    // proceed towards this target to fight them.

    let target_position = target_blob.pos;

    dx = target_position[0] - me_pos[0];

    dy = target_position[1] - me_pos[1];

    possible_dirs = [];

    if (dx >0) possible_dirs.push(['East', 1,0]);

    else if (dx <0) possible_dirs.push(['West', -1,0]);

    if (dy >0) possible_dirs.push(['South',0,1]);

    else if (dy <0) possible_dirs.push(['North',0,-1]);

    chosen_dir = ... similar selection as before...

    // then check for threats (but this move might lead to collision)

    // then choose directions that are safe.

    // similar steps as above.

}

}

// if no pellets or targets are being pursued, perhaps move randomly?

// but also consider energy conservation.

// Else, decide to move in that direction.

// if energy is low, maybe stay.

// ... Now, also consider splitting.

if (me.energy >=150) {

   // split.

    // choose a direction where there is no obstacle or threat?

    // perhaps the direction opposite to crowded areas?

    // but for a first attempt, maybe just split in a random direction.

    // alternatively split towards empty space? Or direction away from threats.

}

// finally, decide direction.
``` }

Wait, this is getting too complex. Perhaps a more simplified approach is needed for the initial code. Alternative plan:
The blob first checks whether to split:
If energy >= 100 (or 150?), then split. Choose direction away from current location, perhaps North. Maybe pick a random direction to spread out. Otherwise:
Prioritize movement to pellets.

Use direction towards closest pellet, avoiding dangerous directions.

If no pellets nearby, move randomly or towards lower-energy threats.

In Drowning in details, here's a more structured approach: FUNCTION:
Check if need to split:
if me.energy >= 100.
Try to split in a direction where there are no immediate threats (like North).

elif has a nearby pellet direction and free space, split towards that direction?

else choose random direction, but avoid walls. Else: Handle movement:
Find best pellet (as per metric).
If exists, compute direction towards it.
Check if that direction is safe (no threat of higher energy there).

proceed in that direction.

else, look for other directions.
if none, choose random non-dangerous direction? else:
look for a target smaller blob. etc.

Alternatively, let's tried to write code step by step.

Here's a draft code:
function(map, near, me, storage) {

```python
// Manage communal relatives array

if (!storage.communal.relatives) storage.communal.relatives = [];

if (!storage.communal.relatives.includes(me.uid)) storage.communal.relatives.push(me.uid);

// Determine threats:

var threats = near.blobs.filter(b => storage.communal.relatives.indexOf(b.uid) <0);

// Find the best pellet:

var best_pellet = null;

let max_metric = -500;

for (var p of near.pellets) {

    let d = taxiDist(me.pos, p.pos); // Using the provided taxiDist.

    let value = p.energy;

    let metric = value - d;

    if (metric > max_metric || (metric === max_metric && d < (best_pellet ? best_pellet.distance : Infinity))) {

        best_pellet = { pos: p.pos, value: value, distance: d };

        max_metric = metric;

    }

}

var chosenDir = null;

// First priority: split

if (me.energy >= 100) {

    // Try to split. We can pick a random direction. Or towards an open spot?

    // Maybe choose a direction with least nearby threats.

    // For simplicity, just pick North.

    chosenDir = SplitNorth;

} else {

    if (best_pellet) {

        // move towards best pellet.

        // compute direction:

        let dx = best_pellet.pos[0] - me.pos[0];

        let dy = best_pellet.pos[1] - me.pos[1];

        let directions = [];

        if (dx >0) directions.push(East); 

        else if (dx <0) directions.push(West);

        if (dy >0) directions.push(South);

        else if (dy <0) directions.push(North);

        // if multiple options, pick larger delta:
````

## Assistant message (target completion)

````
<|block_start|>[0, 16]<|block_end|>
<|summary_start|>Identifies key design concerns: movement cost vs energy, pellet waves becoming scarcer, local vision radius, splitting >50 energy with shared communal storage among relatives, and UID-based relative detection. Proposes strategic components: movement toward pellets (likely nearest; account for path cost), energy management and split threshold (e.g., >150), avoiding/engaging other blobs based on energy comparison and UID (attack weaker, flee stronger, avoid equal), and using communal storage to coordinate relatives (share UIDs, possible pellet/meeting-point info, energy transfer coordination). Ends with first concrete step: when pellets visible, choose closest pellet (taxi distance) and move toward it if within some distance threshold.<|summary_end|>

<|block_start|>[17, 32]<|block_end|>
<|summary_start|>Defines communal UID tracking and behavior logic. On init: if communal.relatives missing, set []; if me.uid absent, push it. All split descendants share communal, so relatives auto-tracked. For each near.blob: if blob.uid in communal.relatives → relative, possibly ignore or assist; else → enemy. Enemy rule: if blob.energy < me.energy → move toward (attack); if blob.energy > me.energy → move away (avoid). Relatives may coordinate via communal storage—share signals, possibly transfer energy to low-energy members. Tentative plans: split when energy >= ~100, choose direction with less congestion; use communal logging of relative positions to cluster or form a central “hub” to dispatch members—both exploratory ideas.<|summary_end|>

<|block_start|>[33, 51]<|block_end|>
<|summary_start|>Finalized greedy heuristic strategy based on net energy gain per move. Decision rules: (1) Survival constraint: do not take energy-expending actions if me.energy < 25. (2) Primary action = pellet collection: for each visible pellet p compute score(p) = p.energy - taxiDist(me.pos, p.pos); select pellet with maximal positive score; move one step toward that pellet (stepping onto its square collects it). (3) If no pellet yields positive score, stand still. Enemy interaction: active chasing of enemies is deferred in this implementation; eating enemies is only opportunistic via collisions induced by pellet-seeking, with explicit avoidance of complex multi-blob convergence logic. No time-dependent phase switching (early pellets vs late hunting) is implemented; communal storage round-tracking omitted. Energy transfer among relatives noted as future/simple adjacency coordination, but excluded from first implementation.<|summary_end|>

<|block_start|>[52, 94]<|block_end|>
<|summary_start|>Defines pellet targeting heuristic assuming movement cost = 1 energy per step: for each visible pellet at (px,py), compute taxicab distance d = |px - x| + |py - y| from current pos (x,y); score = value - d; select pellet with max score, tie-break by smaller d. After target chosen, compute dx = px - x, dy = py - y; candidate move directions: dx>0 -> East, dx<0 -> West; dy>0 -> South, dy<0 -> North (coords: North=-Y, East=+X, South=+Y, West=-X). Move one step toward target by choosing an axis that reduces delta: if both dx and dy nonzero, select axis with larger |dx| vs |dy|; if |dx| == |dy|, deterministically prefer X-axis (East/West). If only one axis nonzero, take that direction. Each move reduces d by 1.<|summary_end|>

<|block_start|>[95, 121]<|block_end|>
<|summary_start|>Introduces collision-avoidance with stronger enemies. For each candidate movement direction (toward chosen pellet), compute new position newpos = me.pos + dir_vector. Using near.blobs, check if any non-relative blob occupies newpos; if yes and its energy >= me.energy, that move is "dangerous" and must be avoided. Describes algorithm: generate directions toward pellet, compute new positions, filter out directions where a stronger non-relative is present. If safe directions remain, choose among them (biased toward pellet goal); if none safe, either stay still or pick another direction not toward pellet. Emphasizes considering only blobs in target cell, not just within radius.<|summary_end|>

<|block_start|>[122, 140]<|block_end|>
<|summary_start|>Refines threat processing: build threats = all near.blobs whose uid not in communal.relatives. For movement evaluation: for each candidate direction, simulate new_x,new_y and check threats; if any threat at that cell with energy > me.energy, mark direction dangerous. Proposed overall sequence: 1) choose best pellet, 2) compute primary direction(s) toward it, 3) test chosen direction for danger; if dangerous, try alternate candy directions (still moving toward pellet) and select first safe; if all dangerous, abandon that pellet and either stand still or choose other movement. Notes complexity and suggests simplified first implementation where pellet target and a small set of directions are evaluated; if unsafe, fallback to idle or random safe move.<|summary_end|>

<|block_start|>[141, 372]<|block_end|>
<|summary_start|>Adds explicit attack behavior plus large code sketch. Priority ordering: avoid death, seek pellets/targets, then split. Code skeleton: in function(map, near, me, storage), ensure communal.relatives exists and contains me.uid. Compute threats as non-relative blobs. Pellet selection: iterate near.pellets, compute dx,dy, distance = |dx|+|dy|, metric = value - distance; track best_pellet with max metric and its distance. Direction selection for pellet: compute dx,dy, build possible_dirs as [dirName, dxStep, dyStep]; choose chosen_dir based on larger |dx| or |dy|. Helper direction2_change(dir_str) maps "North"/"South"/"East"/"West" to [dx,dy]. Compute newpos and check threats: if a threat at newpos with t.energy >= me.energy, mark danger. If chosen_dir dangerous, iterate over possible_dirs to find another safe direction (no threat with energy >= me.energy at its newpos). If none safe, abandon pellet (best_pellet = null). Attack logic: if no pellet dir, find target_blob among threats with energy < me.energy, choosing closest by taxi distance; compute direction similarly toward target_blob and apply same safety checks. Mentions splitting when me.energy >=150, initially as simple random/constant split direction.<|summary_end|>
````