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20b0922 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 | """PAIR: Predictive Agent-Interaction Representation.
The deployment policy is strictly local. Synchronised peer actions and future
local effects are available only to the training-time teacher. Unlike the
previous predictability pilot, PAIR never changes Local-ARCA's action-role
router. A zero-initialised low-rank residual adapter can only modulate the
decoded action tokens, so update zero is exactly the measured Local-ARCA policy.
"""
from __future__ import annotations
import torch
import torch.nn as nn
import torch.nn.functional as F
from stereo_decoder_variants import StereoARCA
class StereoPAIRResidual(StereoARCA):
"""PAIR-conditioned interaction residual over a Local-ARCA policy.
``joint_training=False`` reproduces the conservative frozen-checkpoint
probe. ``joint_training=True`` is the formal from-scratch PAIR policy:
the same local observation/action path, local interaction student and
residual adapter are optimized jointly while the synchronized teacher
remains training-only.
"""
def __init__(
self, *args, event_dim=96, residual_rank=32, joint_training=False, **kwargs
):
super().__init__(*args, **kwargs)
d = self.query.shape[-1]
self.event_dim = event_dim
self.residual_rank = residual_rank
self.local_event_head = nn.Sequential(
nn.LayerNorm(d),
nn.Linear(d, d),
nn.GELU(),
nn.Linear(d, event_dim),
)
self.confidence_head = nn.Sequential(
nn.LayerNorm(d),
nn.Linear(d, d // 2),
nn.GELU(),
nn.Linear(d // 2, 1),
)
self.residual_down = nn.Linear(d, residual_rank, bias=False)
self.event_scale = nn.Linear(event_dim, residual_rank)
self.query_scale = nn.Parameter(torch.zeros(self.query.shape[1], residual_rank))
self.residual_up = nn.Linear(residual_rank, d, bias=False)
# Hard baseline-preservation property: before training, PAIR produces
# exactly Local-ARCA's decoded hidden state and action chunk.
nn.init.zeros_(self.residual_up.weight)
nn.init.zeros_(self.event_scale.weight)
nn.init.zeros_(self.event_scale.bias)
self.last_local_event = None
self.last_confidence = None
self.last_gates = None
self.last_residual_norm = None
self.joint_training = bool(joint_training)
def freeze_local_arca(self):
"""Freeze every parameter inherited from Local-ARCA.
New PAIR modules remain trainable. This makes role-route preservation
structural rather than a soft regularisation preference.
"""
new_prefixes = (
"local_event_head.",
"confidence_head.",
"residual_down.",
"event_scale.",
"residual_up.",
)
for name, parameter in self.named_parameters():
parameter.requires_grad = name == "query_scale" or name.startswith(new_prefixes)
def forward(self, image, depth_mm, qpos, actions=None):
def base_forward():
observation = self._rgbd_tokens(image, depth_mm)
state_vector = self.state(qpos)
gates, auxiliary = self._route(state_vector, observation, image.shape[0])
if actions is not None:
hidden = self.posterior(self.action(actions) + self.pos)
mu, logvar = self.latent(hidden.mean(1)).chunk(2, -1)
logvar = logvar.clamp(-10.0, 5.0)
latent = mu + torch.randn_like(mu) * torch.exp(0.5 * logvar)
else:
mu = logvar = None
latent = torch.zeros(
(image.shape[0], self.z_proj.in_features), device=image.device
)
memory = torch.cat(
(
state_vector.unsqueeze(1),
self.z_proj(latent).unsqueeze(1),
observation,
),
dim=1,
)
decoded = self.decoder(
self.query.expand(image.shape[0], -1, -1),
memory,
observation,
gates,
)
base_action = self.out(decoded)
return observation, state_vector, gates, auxiliary, decoded, base_action, mu, logvar
# The conservative probe structurally freezes Local-ARCA. The formal
# model executes the identical graph with gradients enabled.
if self.joint_training:
observation, state_vector, gates, auxiliary, decoded, base_action, mu, logvar = base_forward()
else:
with torch.no_grad():
observation, state_vector, gates, auxiliary, decoded, base_action, mu, logvar = base_forward()
context = (
self.route_state(state_vector if self.joint_training else state_vector.detach())
+ self.route_observation(
(observation if self.joint_training else observation.detach()).mean(1)
)
)
local_event = F.normalize(
self.local_event_head(context).float(), dim=-1, eps=1e-6
)
confidence = torch.sigmoid(self.confidence_head(context).float())
scale = torch.sigmoid(
self.event_scale(local_event).unsqueeze(1)
+ self.query_scale.unsqueeze(0)
)
residual_hidden = self.residual_up(
F.gelu(
self.residual_down(decoded if self.joint_training else decoded.detach())
)
* scale.to(decoded.dtype)
)
residual_hidden = confidence.to(decoded.dtype).unsqueeze(1) * residual_hidden
action = self.out(
(decoded if self.joint_training else decoded.detach()) + residual_hidden
)
self.last_local_event = local_event
self.last_confidence = confidence
self.last_gates = gates
# Epsilon avoids the undefined d(sqrt(x))/dx at the deliberately
# zero-initialised residual while preserving the reported RMS value.
self.last_residual_norm = (
residual_hidden.float().square().mean() + 1e-12
).sqrt()
return (
action,
mu,
logvar,
auxiliary,
local_event,
confidence,
base_action,
gates,
)
class PairwiseLagInteractionTeacher(nn.Module):
"""Training-only pairwise, directed and lag-selective interaction teacher.
Each target arm attends over all (peer, lag) candidates in its synchronised
unordered team. The target's future *local* effect is privileged training
supervision, never a deployment input.
"""
def __init__(
self,
action_dim,
effect_dim,
lags,
event_dim=96,
width=192,
):
super().__init__()
self.effect_dim = effect_dim
self.lags = tuple(int(value) for value in lags)
self.action_step = nn.Sequential(
nn.Linear(action_dim, width),
nn.GELU(),
nn.Linear(width, width),
)
self.action_feature = nn.Sequential(
nn.LayerNorm(2 * width),
nn.Linear(2 * width, width),
nn.GELU(),
nn.Linear(width, width),
)
self.effect_feature = nn.Sequential(
nn.LayerNorm(effect_dim),
nn.Linear(effect_dim, width),
nn.GELU(),
nn.Linear(width, width),
)
self.lag_embedding = nn.Embedding(len(self.lags), width)
self.pair_value = nn.Sequential(
nn.LayerNorm(4 * width),
nn.Linear(4 * width, width),
nn.GELU(),
nn.Linear(width, event_dim),
)
self.pair_score = nn.Sequential(
nn.LayerNorm(4 * width),
nn.Linear(4 * width, width),
nn.GELU(),
nn.Linear(width, 1),
)
self.own_effect = nn.Sequential(
nn.Linear(width, width),
nn.GELU(),
nn.Linear(width, len(self.lags) * effect_dim),
)
self.interaction_effect = nn.Sequential(
nn.Linear(event_dim, width),
nn.GELU(),
nn.Linear(width, len(self.lags) * effect_dim),
)
def action_features(self, actions):
delta = actions[:, 1:] - actions[:, :-1]
encoded = self.action_step(delta.float())
return self.action_feature(
torch.cat((encoded.mean(1), encoded.square().mean(1).sqrt()), dim=-1)
)
def forward(self, actions, future_effects, groups):
"""Return event, predictive-gain confidence target and diagnostics."""
own = self.action_features(actions)
effect_features = self.effect_feature(future_effects.float())
events = torch.zeros(
(len(actions), self.interaction_effect[0].in_features),
device=actions.device,
dtype=torch.float32,
)
attention_entropy = []
for group in groups.unique(sorted=True):
ids = (groups == group).nonzero(as_tuple=False).flatten()
values = own.index_select(0, ids)
group_effects = effect_features.index_select(0, ids)
for local_index, target_id in enumerate(ids):
candidates, scores = [], []
target = values[local_index]
peer_indices = [
index for index in range(len(ids)) if index != local_index
]
if not peer_indices:
continue
for peer_index in peer_indices:
peer = values[peer_index]
for lag_index in range(len(self.lags)):
effect = group_effects[local_index, lag_index]
lag = self.lag_embedding.weight[lag_index]
pair = torch.cat(
(target, peer, target - peer, effect + lag), dim=-1
)
candidates.append(self.pair_value(pair))
scores.append(self.pair_score(pair))
score = torch.cat(scores, dim=0)
weight = score.softmax(dim=0)
candidate = torch.stack(candidates, dim=0)
event = (weight.unsqueeze(-1) * candidate).sum(0)
events[target_id] = event.float()
attention_entropy.append(
-(weight.clamp_min(1e-8).log() * weight).sum()
)
teacher_event = F.normalize(events, dim=-1, eps=1e-6)
target = future_effects.float().flatten(1)
own_prediction = self.own_effect(own)
full_prediction = own_prediction + self.interaction_effect(teacher_event)
own_error = (own_prediction - target).square().mean(-1)
full_error = (full_prediction - target).square().mean(-1)
# Only the locally consequential part receives a strong deployment
# confidence target. Detaching prevents the teacher from gaming it.
gain = ((own_error - full_error) / own_error.clamp_min(1e-6)).clamp(0, 1).detach()
zero = own_error.new_zeros(())
entropy = torch.stack(attention_entropy).mean() if attention_entropy else zero
return (
teacher_event,
gain.unsqueeze(-1),
own_error.mean(),
full_error.mean(),
entropy,
)
def interaction_alignment(student_event, teacher_event, temperature=0.15):
student = F.normalize(student_event.float(), dim=-1, eps=1e-6)
teacher = F.normalize(teacher_event.detach().float(), dim=-1, eps=1e-6)
logits = student @ teacher.t() / temperature
target = torch.arange(len(student), device=student.device)
return 0.5 * (
F.cross_entropy(logits, target) + F.cross_entropy(logits.t(), target)
)
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