diff --git a/Project.toml b/Project.toml
index f7a74a2d..c69eb30a 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,7 +1,7 @@
 name = "AdvancedPS"
 uuid = "576499cb-2369-40b2-a588-c64705576edc"
 authors = ["TuringLang"]
-version = "0.4"
+version = "0.4.0"
 
 [deps]
 AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001"
diff --git a/examples/gaussian-ssm/Manifest.toml b/examples/gaussian-ssm/Manifest.toml
deleted file mode 100644
index e077aa73..00000000
--- a/examples/gaussian-ssm/Manifest.toml
+++ /dev/null
@@ -1,1077 +0,0 @@
-# This file is machine-generated - editing it directly is not advised
-
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diff --git a/examples/gaussian-ssm/script.jl b/examples/gaussian-ssm/script.jl
index f75057e0..8f46e70c 100644
--- a/examples/gaussian-ssm/script.jl
+++ b/examples/gaussian-ssm/script.jl
@@ -1,13 +1,75 @@
 # # Gaussian State Space Model
 using AdvancedPS
-using AbstractMCMC
+using Random
 using Distributions
-using Libtask: produce
-using StatsFuns
-
 using Plots
 
-x = [1, 2, 3]
-y = [2, 5, 1]
+# Parameters
+Parameters = @NamedTuple begin
+    a::Float64
+    q::Float64
+    r::Float64
+end
+
+a = 0.9   # Scale
+q = 0.32  # State variance
+r = 1     # Observation variance
+Tₘ = 300  # Number of observation
+Nₚ = 50   # Number of particles
+Nₛ = 1000 # Number of samples
+seed = 9  # Reproduce everything
+
+θ₀ = Parameters((a, q, r))
+
+mutable struct NonLinearTimeSeries <: AdvancedPS.AbstractStateSpaceModel
+    X::Vector{Float64}
+    θ::Parameters
+    NonLinearTimeSeries(θ::Parameters) = new(Vector{Float64}(), θ)
+    NonLinearTimeSeries() = new(Vector{Float64}(), θ₀)
+end
+
+f(m::NonLinearTimeSeries, state, t) = Normal(m.θ.a * state, m.θ.q) # Transition density
+g(m::NonLinearTimeSeries, state, t) = Normal(state, m.θ.r) # Observation density
+f₀(m::NonLinearTimeSeries) = Normal(0, m.θ.q / (1 - m.θ.a^2)) # Initial state density
+
+AdvancedPS.initialization(model::NonLinearTimeSeries) = f₀(model)
+AdvancedPS.transition(model::NonLinearTimeSeries, state, step) = f(model, state, step)
+function AdvancedPS.observation(model::NonLinearTimeSeries, state, step)
+    return logpdf(g(model, state, step), y[step])
+end # Return log-pdf of the obs
+AdvancedPS.isdone(::NonLinearTimeSeries, step) = step > Tₘ
+
+# Generate some synthetic data
+rng = Random.MersenneTwister(seed)
+
+x = zeros(Tₘ)
+y = zeros(Tₘ)
+
+reference = NonLinearTimeSeries(θ₀) # Reference model
+x[1] = rand(rng, f₀(reference))
+for t in 1:Tₘ
+    if t < Tₘ
+        x[t + 1] = rand(rng, f(reference, x[t], t))
+    end
+    y[t] = rand(rng, g(reference, x[t], t))
+end
+
+plot(x; label="x", color=:black)
+plot(y; label="y", color=:black)
+
+# Setup up a particle Gibbs sampler
+model = NonLinearTimeSeries(θ₀)
+pgas = AdvancedPS.PGAS(Nₚ)
+chains = sample(rng, model, pgas, Nₛ)
+
+particles = hcat([chain.trajectory.model.X for chain in chains]...) # Concat all sampled states
+mean_trajectory = mean(particles; dims=2)
+
+scatter(particles; label=false, opacity=0.01, color=:black)
+plot!(x; color=:red, label="Original Trajectory")
+plot!(mean_trajectory; color=:blue, label="Posterior mean", opacity=0.9)
 
-plot(x, y)
+# Compute mixing rate
+update_rate = sum(abs.(diff(particles; dims=2)) .> 0; dims=2) / Nₛ
+plot(update_rate; label=false, ylim=[0, 1])
+hline!([1 - 1 / Nₚ]; label=false)
diff --git a/src/AdvancedPS.jl b/src/AdvancedPS.jl
index e1c7832d..c0abcfb2 100644
--- a/src/AdvancedPS.jl
+++ b/src/AdvancedPS.jl
@@ -7,10 +7,15 @@ using Random: Random
 using StatsFuns: StatsFuns
 using Random123: Random123
 
+""" Abstract type for an abstract model formulated in the state space form
+"""
+abstract type AbstractStateSpaceModel <: AbstractMCMC.AbstractModel end
+
 include("resampling.jl")
 include("rng.jl")
+include("model.jl")
 include("container.jl")
+include("pgas.jl")
 include("smc.jl")
-include("model.jl")
 
 end
diff --git a/src/container.jl b/src/container.jl
index e621a21f..bc0697fe 100644
--- a/src/container.jl
+++ b/src/container.jl
@@ -1,82 +1,13 @@
-struct Trace{F,R<:TracedRNG}
-    f::F
-    task::Libtask.TapedTask
-    rng::R
-end
-
-const Particle = Trace
-
-function Trace(f, rng::TracedRNG)
-    task = Libtask.TapedTask(f, rng)
-
-    # add backward reference
-    newtrace = Trace(f, task, rng)
-    addreference!(task.task, newtrace)
-
-    return newtrace
-end
-
-function Trace(f, task::Libtask.TapedTask)
-    return Trace(f, task, TracedRNG())
-end
-
-# Copy task
-Base.copy(trace::Trace) = Trace(trace.f, copy(trace.task), deepcopy(trace.rng))
-
 # step to the next observe statement and
 # return the log probability of the transition (or nothing if done)
-function advance!(t::Trace, isref::Bool)
+function advance!(t::GenericTrace, isref::Bool=false)
     isref ? load_state!(t.rng) : save_state!(t.rng)
     inc_counter!(t.rng)
 
     # Move to next step
-    return Libtask.consume(t.task)
+    return Libtask.consume(t.model.ctask)
 end
 
-# reset log probability
-reset_logprob!(t::Trace) = nothing
-
-reset_model(f) = deepcopy(f)
-delete_retained!(f) = nothing
-
-# Task copying version of fork for Trace.
-function fork(trace::Trace, isref::Bool=false)
-    newtrace = copy(trace)
-    isref && delete_retained!(newtrace.f)
-
-    # add backward reference
-    addreference!(newtrace.task.task, newtrace)
-
-    return newtrace
-end
-
-# PG requires keeping all randomness for the reference particle
-# Create new task and copy randomness
-function forkr(trace::Trace)
-    newf = reset_model(trace.f)
-    Random123.set_counter!(trace.rng, 1)
-
-    task = Libtask.TapedTask(newf, trace.rng)
-
-    # add backward reference
-    newtrace = Trace(newf, task, trace.rng)
-    addreference!(task.task, newtrace)
-
-    return newtrace
-end
-
-# create a backward reference in task_local_storage
-function addreference!(task::Task, trace::Trace)
-    if task.storage === nothing
-        task.storage = IdDict()
-    end
-    task.storage[:__trace] = trace
-
-    return task
-end
-
-current_trace() = current_task().storage[:__trace]
-
 """
 Data structure for particle filters
 - `effectiveSampleSize(pc :: ParticleContainer)`: Return the effective sample size of the particles in `pc`
@@ -135,6 +66,13 @@ function Base.copy(pc::ParticleContainer)
     return ParticleContainer(vals, logWs, rng)
 end
 
+"""
+    update_ref!(particle::Trace, pc::ParticleContainer)
+
+Update reference trajectory. Defaults to `nothing`
+"""
+update_ref!(particle::Trace, pc::ParticleContainer) = nothing
+
 """
     reset_logweights!(pc::ParticleContainer)
 
@@ -237,19 +175,16 @@ of the particle `weights`. For Particle Gibbs sampling, one can provide a refere
 `ref` that is ensured to survive the resampling step.
 """
 function resample_propagate!(
-    rng::Random.AbstractRNG,
+    ::Random.AbstractRNG,
     pc::ParticleContainer,
-    randcat=resample_systematic,
+    randcat=DEFAULT_RESAMPLER,
     ref::Union{Particle,Nothing}=nothing;
     weights=getweights(pc),
 )
-    # check that weights are not NaN
-    @assert !any(isnan, weights)
-
     # sample ancestor indices
     n = length(pc)
     nresamples = ref === nothing ? n : n - 1
-    indx = randcat(rng, weights, nresamples)
+    indx = randcat(pc.rng, weights, nresamples)
 
     # count number of children for each particle
     num_children = zeros(Int, n)
@@ -263,16 +198,18 @@ function resample_propagate!(
     j = 0
     @inbounds for i in 1:n
         ni = num_children[i]
-
         if ni > 0
             # fork first child
             pi = particles[i]
             isref = pi === ref
             p = isref ? fork(pi, isref) : pi
-            nseeds = isref ? ni - 1 : ni
 
-            seeds = split(p.rng.rng.key, nseeds)
-            !isref && Random.seed!(p.rng, seeds[1])
+            key = isref ? safe_get_refseed(ref.rng) : state(p.rng.rng) # Pick up the alternative rng stream if using the reference particle
+            nsplits = isref ? ni + 1 : ni # We need one more seed to refresh the alternative rng stream
+            seeds = split(key, nsplits)
+            isref && safe_set_refseed!(ref.rng, seeds[end]) # Refresh the alternative rng stream
+
+            Random.seed!(p.rng, seeds[1])
 
             children[j += 1] = p
             # fork additional children
@@ -287,6 +224,7 @@ function resample_propagate!(
     if ref !== nothing
         # Insert the retained particle. This is based on the replaying trick for efficiency
         # reasons. If we implement PG using task copying, we need to store Nx * T particles!
+        update_ref!(ref, pc)
         @inbounds children[n] = ref
     end
 
diff --git a/src/model.jl b/src/model.jl
index d2028389..d33e2ba9 100644
--- a/src/model.jl
+++ b/src/model.jl
@@ -1,3 +1,90 @@
+"""
+    GenericModel{F}
+
+State wrapper to hold `Libtask.CTask` model initiated from `f`
+"""
+struct GenericModel{F} <: AbstractMCMC.AbstractModel
+    f::F
+    ctask::Libtask.TapedTask{F}
+end
+
+GenericModel(f, args...) = GenericModel(f, Libtask.TapedTask(f, args...))
+Base.copy(model::GenericModel) = GenericModel(model.f, copy(model.ctask))
+
+"""
+    Trace{F,R}
+"""
+struct Trace{F,R}
+    model::F
+    rng::R
+
+    Trace(model::F, rng::R) where {F,R} = new{F,R}(model, rng) # Bring back the default since we override
+
+    function Trace(model::F, rng::R) where {F<:GenericModel,R}
+        # add backward reference
+        newtrace = new{F,R}(model, rng)
+        addreference!(model.ctask.task, newtrace)
+        return newtrace
+    end
+end
+
+const Particle = Trace
+
+const SSMTrace{R} = Trace{<:AbstractStateSpaceModel,R}
+const GenericTrace{R} = Trace{<:GenericModel,R}
+
+# Copy task
+Base.copy(trace::GenericTrace) = Trace(copy(trace.model), deepcopy(trace.rng))
+
+# create a backward reference in task_local_storage
+function addreference!(task::Task, trace::GenericTrace)
+    if task.storage === nothing
+        task.storage = IdDict()
+    end
+    task.storage[:__trace] = trace
+
+    return task
+end
+
+current_trace() = current_task().storage[:__trace]
+
+# Task copying version of fork for Trace.
+function fork(trace::GenericTrace, isref::Bool=false)
+    newtrace = copy(trace)
+    isref && delete_retained!(newtrace.model.f)
+
+    # add backward reference
+    addreference!(newtrace.model.ctask.task, newtrace)
+    return newtrace
+end
+
+# PG requires keeping all randomness for the reference particle
+# Create new task and copy randomness
+function forkr(trace::GenericTrace)
+    newf = reset_model(trace.model.f)
+    Random123.set_counter!(trace.rng, 1)
+
+    ctask = Libtask.TapedTask(newf, trace.rng)
+    new_tapedmodel = GenericModel(newf, ctask)
+
+    # add backward reference
+    newtrace = Trace(new_tapedmodel, trace.rng)
+    addreference!(ctask.task, newtrace)
+    gen_refseed!(newtrace)
+    return newtrace
+end
+
+function gen_refseed!(part::Particle)
+    seed = split(state(part.rng.rng), 1)
+    return safe_set_refseed!(part.rng, seed[1])
+end
+
+# reset log probability
+reset_logprob!(::Particle) = nothing
+
+reset_model(f) = deepcopy(f)
+delete_retained!(f) = nothing
+
 """
     observe(dist::Distribution, x)
 
diff --git a/src/pgas.jl b/src/pgas.jl
new file mode 100644
index 00000000..1ed2a838
--- /dev/null
+++ b/src/pgas.jl
@@ -0,0 +1,151 @@
+""" 
+    initialization(model::AbstractStateSpaceModel)
+
+Define the distribution of the initial state of the State Space Model
+"""
+function initialization end
+
+""" 
+    transition(model::AbstractStateSpaceModel, state, step)
+
+Define the transition density of the State Space Model
+Must return `nothing` if it consumed all the data
+"""
+function transition end
+
+"""
+    observation(model::AbstractStateSpaceModel, state, step)
+
+Return the log-likelihood of the observed measurement conditional on the current state of the model. 
+Must return `nothing` if it consumed all the data
+"""
+function observation end
+
+""" 
+    isdone(model::AbstractStateSpaceModel, step)
+
+Return `true` if model reached final state else `false`
+"""
+function isdone end
+
+"""
+    previous_state(trace::SSMTrace)
+
+Return `Xₜ₋₁` or `nothing` from `model`
+"""
+function previous_state(trace::SSMTrace)
+    return trace.model.X[current_step(trace) - 1]
+end
+
+function past_idx(trace::SSMTrace)
+    return 1:(current_step(trace) - 1)
+end
+
+"""
+    current_step(model::AbstractStateSpaceModel)
+
+Return current model step
+"""
+current_step(trace::SSMTrace) = trace.rng.count
+
+"""
+    transition_logweight(model::AbstractStateSpaceModel, x)
+
+Get the log weight of the transition from previous state of `model` to `x`
+"""
+function transition_logweight(particle::SSMTrace, x)
+    score = Distributions.logpdf(
+        transition(
+            particle.model,
+            particle.model.X[current_step(particle) - 2],
+            current_step(particle) - 2,
+        ),
+        x,
+    )
+    return score
+end
+
+"""
+    get_ancestor_logweights(pc::ParticleContainer{F,R}, x) where {F<:SSMTrace,R}
+
+Get the ancestor log weights for each particle in `pc`
+"""
+function get_ancestor_logweights(pc::ParticleContainer{<:SSMTrace}, x, weights)
+    nparticles = length(pc.vals)
+
+    logweights = map(1:nparticles) do i
+        transition_logweight(pc.vals[i], x) + weights[i]
+    end
+    return logweights
+end
+
+""" 
+    advance!(particle::SSMTrace, isref::Bool=false)
+
+Return the log-probability of the transition nothing if done
+"""
+function advance!(particle::SSMTrace, isref::Bool=false)
+    isref ? load_state!(particle.rng) : save_state!(particle.rng)
+
+    model = particle.model
+    running_step = current_step(particle)
+    isdone(model, running_step) && return nothing
+
+    if !isref
+        if running_step == 1
+            new_state = rand(particle.rng, initialization(model)) # Generate initial state, maybe fallback to 0 if initialization is not defined
+        else
+            current_state = model.X[running_step - 1]
+            new_state = rand(particle.rng, transition(model, current_state, running_step))
+        end
+    else
+        new_state = model.X[running_step] # We need the current state from the reference particle
+    end
+
+    score = observation(model, new_state, running_step)
+
+    # accept transition
+    !isref && push!(model.X, new_state)
+    inc_counter!(particle.rng) # Increase rng counter, we use it as the model `time` index instead of handling two distinct counters
+
+    return score
+end
+
+function truncate!(particle::SSMTrace)
+    model = particle.model
+    idx = past_idx(particle)
+    model.X = model.X[idx]
+    particle.rng.keys = particle.rng.keys[idx]
+    return model
+end
+
+function fork(particle::SSMTrace, isref::Bool)
+    model = deepcopy(particle.model)
+    new_particle = Trace(model, deepcopy(particle.rng))
+    isref && truncate!(new_particle) # Forget the rest of the reference trajectory
+    return new_particle
+end
+
+function forkr(particle::SSMTrace)
+    Random123.set_counter!(particle.rng, 1)
+    newtrace = Trace(deepcopy(particle.model), deepcopy(particle.rng))
+    gen_refseed!(newtrace)
+    return newtrace
+end
+
+function update_ref!(ref::SSMTrace, pc::ParticleContainer{<:SSMTrace})
+    current_step(ref) <= 2 && return nothing # At the beginning of step + 1 since we start at 1
+    isdone(ref.model, current_step(ref)) && return nothing
+
+    ancestor_weights = get_ancestor_logweights(
+        pc, ref.model.X[current_step(ref) - 1], pc.logWs
+    )
+    norm_weights = StatsFuns.softmax(ancestor_weights)
+
+    ancestor_index = rand(pc.rng, Distributions.Categorical(norm_weights))
+    ancestor = pc.vals[ancestor_index]
+
+    idx = past_idx(ref)
+    ref.model.X[idx] = ancestor.model.X[idx]
+    return ref.rng.keys[idx] = ancestor.rng.keys[idx]
+end
diff --git a/src/resampling.jl b/src/resampling.jl
index 6ad00291..96969876 100644
--- a/src/resampling.jl
+++ b/src/resampling.jl
@@ -7,21 +7,6 @@
 #  - http://people.isy.liu.se/rt/schon/Publications/HolSG2006.pdf
 # Code adapted from: http://uk.mathworks.com/matlabcentral/fileexchange/24968-resampling-methods-for-particle-filtering
 
-"""
-    ResampleWithESSThreshold{R,T<:Real}
-
-Perform resampling using `R` if the effective sample size is below `T`.
-By default we use `resample_systematic` with a threshold of 0.5
-"""
-struct ResampleWithESSThreshold{R,T<:Real}
-    resampler::R
-    threshold::T
-end
-
-function ResampleWithESSThreshold(resampler=resample_systematic)
-    return ResampleWithESSThreshold(resampler, 0.5)
-end
-
 # More stable, faster version of rand(Categorical)
 function randcat(rng::Random.AbstractRNG, p::AbstractVector{<:Real})
     T = eltype(p)
@@ -196,3 +181,24 @@ function resample_systematic(
 
     return samples
 end
+
+const DEFAULT_RESAMPLER = resample_systematic
+
+"""
+    ResampleWithESSThreshold{R,T<:Real}
+
+Perform resampling using `R` if the effective sample size is below `T`.
+By default we use `resample_systematic` with a threshold of 0.5
+"""
+struct ResampleWithESSThreshold{R,T<:Real}
+    resampler::R
+    threshold::T
+end
+
+function ResampleWithESSThreshold(resampler=DEFAULT_RESAMPLER)
+    return ResampleWithESSThreshold(resampler, 0.5)
+end
+
+function ResampleWithESSThreshold(threshold::T) where {T<:Real}
+    return ResampleWithESSThreshold(DEFAULT_RESAMPLER, threshold)
+end
diff --git a/src/rng.jl b/src/rng.jl
index 62d9637f..83d0cb9e 100644
--- a/src/rng.jl
+++ b/src/rng.jl
@@ -13,6 +13,8 @@ mutable struct TracedRNG{R,N,T<:Random123.AbstractR123} <: Random.AbstractRNG
     rng::T
     "Array of keys"
     keys::Array{R,N}
+    "Reference particle alternative seed"
+    refseed::Union{R,Nothing}
 end
 
 """
@@ -21,7 +23,7 @@ Create a `TracedRNG` with `r` as the inner RNG.
 """
 function TracedRNG(r::Random123.AbstractR123=_BASE_RNG())
     Random123.set_counter!(r, 0)
-    return TracedRNG(1, r, Random123.seed_type(r)[])
+    return TracedRNG(1, r, Random123.seed_type(r)[], nothing)
 end
 
 # Connect to the Random API
@@ -86,7 +88,21 @@ end
 state(rng::Random123.Philox2x) = rng.key
 state(rng::Random123.Philox4x) = (rng.key1, rng.key2)
 
-Base.copy(r::TracedRNG) = TracedRNG(r.count, copy(r.rng), deepcopy(r.keys))
+function Base.copy(rng::TracedRNG)
+    return TracedRNG(rng.count, copy(rng.rng), deepcopy(rng.keys), rng.refseed)
+end
+
+# Add an extra seed to the reference particle keys array to use as an alternative stream 
+# (we don't need to tack this one)
+#
+# We have to be careful when spliting the reference particle. 
+# Since we don't know the seed tree from the previous SMC run we cannot reuse any of the intermediate seed
+# in the TracedRNG container. We might collide with a previous seed and the children particle would collapse
+# to the reference particle. A solution to solve this is to have an extra stream attached to the reference particle 
+# that we only use to seed the children of the reference particle. 
+#
+safe_set_refseed!(rng::TracedRNG{R}, seed::R) where {R} = rng.refseed = seed
+safe_get_refseed(rng::TracedRNG) = rng.refseed
 
 """
     set_counter!(r::TracedRNG, n::Integer)
diff --git a/src/smc.jl b/src/smc.jl
index e885a226..f8e525d8 100644
--- a/src/smc.jl
+++ b/src/smc.jl
@@ -18,7 +18,7 @@ SMC(nparticles::Int) = SMC(nparticles, ResampleWithESSThreshold())
 function SMC(nparticles::Int, resampler, threshold::Real)
     return SMC(nparticles, ResampleWithESSThreshold(resampler, threshold))
 end
-SMC(nparticles::Int, threshold::Real) = SMC(nparticles, resample_systematic, threshold)
+SMC(nparticles::Int, threshold::Real) = SMC(nparticles, DEFAULT_RESAMPLER, threshold)
 
 struct SMCSample{P,W,L}
     trajectories::P
@@ -37,10 +37,14 @@ function AbstractMCMC.sample(
         @warn "keyword arguments $(keys(kwargs)) are not supported by `SMC`"
     end
 
+    traces = map(1:(sampler.nparticles)) do i
+        trng = TracedRNG()
+        tmodel = GenericModel(model, trng)
+        Trace(tmodel, trng)
+    end
+
     # Create a set of particles.
-    particles = ParticleContainer(
-        [Trace(model, TracedRNG()) for _ in 1:(sampler.nparticles)], TracedRNG(), rng
-    )
+    particles = ParticleContainer(traces, TracedRNG(), rng)
 
     # Perform particle sweep.
     logevidence = sweep!(rng, particles, sampler.resampler)
@@ -70,7 +74,7 @@ PG(nparticles::Int) = PG(nparticles, ResampleWithESSThreshold())
 function PG(nparticles::Int, resampler, threshold::Real)
     return PG(nparticles, ResampleWithESSThreshold(resampler, threshold))
 end
-PG(nparticles::Int, threshold::Real) = PG(nparticles, resample_systematic, threshold)
+PG(nparticles::Int, threshold::Real) = PG(nparticles, DEFAULT_RESAMPLER, threshold)
 
 struct PGState{T}
     trajectory::T
@@ -81,50 +85,74 @@ struct PGSample{T,L}
     logevidence::L
 end
 
+struct PGAS{R} <: AbstractMCMC.AbstractSampler
+    """Number of particles."""
+    nparticles::Int
+    """Resampling algorithm."""
+    resampler::R
+end
+
+PGAS(nparticles::Int) = PGAS(nparticles, ResampleWithESSThreshold(1.0))
+
 function AbstractMCMC.step(
-    rng::Random.AbstractRNG, model::AbstractMCMC.AbstractModel, sampler::PG; kwargs...
+    rng::Random.AbstractRNG,
+    model::AbstractMCMC.AbstractModel,
+    sampler::PG,
+    state::Union{PGState,Nothing}=nothing;
+    kwargs...,
 )
     # Create a new set of particles.
-    particles = ParticleContainer(
-        [Trace(model, TracedRNG()) for _ in 1:(sampler.nparticles)], TracedRNG(), rng
-    )
+    nparticles = sampler.nparticles
+    isref = !isnothing(state)
+
+    traces = map(1:nparticles) do i
+        if i == nparticles && isref
+            # Create reference trajectory.
+            forkr(state.trajectory)
+        else
+            trng = TracedRNG()
+            Trace(GenericModel(model, trng), trng)
+        end
+    end
+
+    particles = ParticleContainer(traces, TracedRNG(), rng)
 
     # Perform a particle sweep.
-    logevidence = sweep!(rng, particles, sampler.resampler)
+    reference = isref ? particles.vals[nparticles] : nothing
+    logevidence = sweep!(rng, particles, sampler.resampler, reference)
 
     # Pick a particle to be retained.
-    trajectory = rand(rng, particles)
+    newtrajectory = rand(rng, particles)
 
-    return PGSample(trajectory, logevidence), PGState(trajectory)
+    return PGSample(newtrajectory, logevidence), PGState(newtrajectory)
 end
 
 function AbstractMCMC.step(
     rng::Random.AbstractRNG,
-    model::AbstractMCMC.AbstractModel,
-    sampler::PG,
-    state::PGState;
+    model::AbstractStateSpaceModel,
+    sampler::PGAS,
+    state::Union{PGState,Nothing}=nothing;
     kwargs...,
 )
     # Create a new set of particles.
     nparticles = sampler.nparticles
+    isref = !isnothing(state)
 
-    x = map(1:nparticles) do i
-        if i == nparticles
+    traces = map(1:nparticles) do i
+        if i == nparticles && isref
             # Create reference trajectory.
-            forkr(state.trajectory)
+            forkr(deepcopy(state.trajectory))
         else
-            Trace(model, TracedRNG())
+            Trace(deepcopy(model), TracedRNG())
         end
     end
-
-    reference = x[end]
-    particles = ParticleContainer(x, TracedRNG(), rng)
+    particles = ParticleContainer(traces, TracedRNG(), rng)
 
     # Perform a particle sweep.
+    reference = isref ? particles.vals[nparticles] : nothing
     logevidence = sweep!(rng, particles, sampler.resampler, reference)
 
     # Pick a particle to be retained.
-    newtrajectory = rand(rng, particles)
-
+    newtrajectory = rand(particles.rng, particles)
     return PGSample(newtrajectory, logevidence), PGState(newtrajectory)
 end
diff --git a/test/container.jl b/test/container.jl
index 333db404..9eea2505 100644
--- a/test/container.jl
+++ b/test/container.jl
@@ -22,7 +22,11 @@
 
         # Create particle container.
         logps = [0.0, -1.0, -2.0]
-        particles = [AdvancedPS.Trace(fpc(logp), AdvancedPS.TracedRNG()) for logp in logps]
+        particles = map(logps) do logp
+            trng = AdvancedPS.TracedRNG()
+            tmodel = AdvancedPS.GenericModel(fpc(logp), trng)
+            AdvancedPS.Trace(tmodel, trng)
+        end
         pc = AdvancedPS.ParticleContainer(particles)
 
         # Initial state.
@@ -52,12 +56,20 @@
         @test AdvancedPS.logZ(pc) ≈ log(sum(exp, 2 .* logps))
 
         # Resample and propagate particles with reference particle
-        particles_ref = [
-            AdvancedPS.Trace(fpc(logp), AdvancedPS.TracedRNG()) for logp in logps
-        ]
+        particles_ref = map(logps) do logp
+            trng = AdvancedPS.TracedRNG()
+            tmodel = AdvancedPS.GenericModel(fpc(logp), trng)
+            AdvancedPS.Trace(tmodel, trng)
+        end
         pc_ref = AdvancedPS.ParticleContainer(particles_ref)
+
+        selected = particles_ref[end] # Replicate life cycle of the reference particle
+        AdvancedPS.advance!(selected)
+        ref = AdvancedPS.forkr(selected)
+        pc_ref.vals[end] = ref
+
         AdvancedPS.resample_propagate!(
-            Random.GLOBAL_RNG, pc_ref, AdvancedPS.resample_systematic, particles_ref[end]
+            Random.GLOBAL_RNG, pc_ref, AdvancedPS.resample_systematic, ref
         )
         @test pc_ref.logWs == zeros(3)
         @test AdvancedPS.getweights(pc_ref) == fill(1 / 3, 3)
@@ -109,17 +121,19 @@
         end
 
         # Test task copy version of trace
-        tr = AdvancedPS.Trace(f2, AdvancedPS.TracedRNG())
+        trng = AdvancedPS.TracedRNG()
+        tmodel = AdvancedPS.GenericModel(f2, trng)
+        tr = AdvancedPS.Trace(tmodel, trng)
 
-        consume(tr.task)
-        consume(tr.task)
+        consume(tr.model.ctask)
+        consume(tr.model.ctask)
 
         a = AdvancedPS.fork(tr)
-        consume(a.task)
-        consume(a.task)
+        consume(a.model.ctask)
+        consume(a.model.ctask)
 
-        @test consume(tr.task) == 2
-        @test consume(a.task) == 4
+        @test consume(tr.model.ctask) == 2
+        @test consume(a.model.ctask) == 4
     end
 
     @testset "seed container" begin
@@ -129,7 +143,11 @@
         n = 3
         rng = Random.MersenneTwister(seed)
 
-        particles = [AdvancedPS.Trace(dummy, AdvancedPS.TracedRNG()) for _ in 1:n]
+        particles = map(1:n) do _
+            trng = AdvancedPS.TracedRNG()
+            tmodel = AdvancedPS.GenericModel(dummy, trng)
+            AdvancedPS.Trace(tmodel, trng)
+        end
         pc = AdvancedPS.ParticleContainer(particles, AdvancedPS.TracedRNG())
 
         AdvancedPS.seed_from_rng!(pc, rng)
diff --git a/test/pgas.jl b/test/pgas.jl
new file mode 100644
index 00000000..cd319a4b
--- /dev/null
+++ b/test/pgas.jl
@@ -0,0 +1,73 @@
+@testset "pgas.jl" begin
+    mutable struct Params
+        a::Float64
+        q::Float64
+        r::Float64
+    end
+
+    mutable struct BaseModel <: AdvancedPS.AbstractStateSpaceModel
+        X::Vector{Float64}
+        θ::Params
+        BaseModel(params::Params) = new(Vector{Float64}(), params)
+    end
+
+    AdvancedPS.initialization(model::BaseModel) = Normal(0, model.θ.q)
+    function AdvancedPS.transition(model::BaseModel, state, step)
+        return Distributions.Normal(model.θ.a * state, model.θ.q)
+    end
+    function AdvancedPS.observation(model::BaseModel, state, step)
+        return Distributions.logpdf(Distributions.Normal(state, model.θ.r), 0)
+    end
+
+    AdvancedPS.isdone(::BaseModel, step) = step > 3
+
+    @testset "fork reference" begin
+        model = BaseModel(Params(0.9, 0.32, 1))
+        part = AdvancedPS.Trace(model, AdvancedPS.TracedRNG())
+
+        AdvancedPS.advance!(part)
+        AdvancedPS.advance!(part)
+        AdvancedPS.advance!(part)
+        @test length(part.model.X) == 3
+
+        trajectory = deepcopy(part.model.X)
+        ref = AdvancedPS.forkr(part)
+        @test all(trajectory .≈ ref.model.X)
+
+        AdvancedPS.advance!(ref)
+        new_part = AdvancedPS.fork(ref, true)
+        @test length(new_part.model.X) == 1
+        @test new_part.model.X[1] ≈ ref.model.X[1]
+    end
+
+    @testset "update reference" begin
+        base_rng = Random.MersenneTwister(31)
+        particles = [
+            AdvancedPS.Trace(BaseModel(Params(0.9, 0.31, 1)), AdvancedPS.TracedRNG()) for
+            _ in 1:3
+        ]
+        resampler = AdvancedPS.ResampleWithESSThreshold(1.0)
+
+        part = particles[3]
+        AdvancedPS.advance!(part)
+        AdvancedPS.advance!(part)
+        AdvancedPS.advance!(part)
+
+        ref = AdvancedPS.forkr(part)
+        particles[3] = ref
+        pc = AdvancedPS.ParticleContainer(particles, AdvancedPS.TracedRNG(), base_rng)
+
+        AdvancedPS.reweight!(pc, ref)
+        AdvancedPS.resample_propagate!(base_rng, pc, resampler, ref)
+
+        AdvancedPS.reweight!(pc, ref)
+        pc.logWs = [-Inf, 0, -Inf] # Force ancestor update to second particle
+        AdvancedPS.resample_propagate!(base_rng, pc, resampler, ref)
+
+        AdvancedPS.reweight!(pc, ref)
+        @test all(pc.vals[2].model.X[1:2] .≈ ref.model.X[1:2])
+
+        terminal_values = [part.model.X[3] for part in pc.vals]
+        @test length(Set(terminal_values)) == 3 # All distinct
+    end
+end
diff --git a/test/runtests.jl b/test/runtests.jl
index 32adc400..d7058062 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -18,4 +18,7 @@ using Test
     @testset "RNG tests" begin
         include("rng.jl")
     end
+    @testset "PG-AS" begin
+        include("pgas.jl")
+    end
 end
diff --git a/test/smc.jl b/test/smc.jl
index d8765133..8bb2419d 100644
--- a/test/smc.jl
+++ b/test/smc.jl
@@ -93,7 +93,7 @@
 
         chains_smc = sample(TestModel(), AdvancedPS.SMC(100))
 
-        @test all(isone(p.f.x) for p in chains_smc.trajectories)
+        @test all(isone(p.model.f.x) for p in chains_smc.trajectories)
         @test chains_smc.logevidence ≈ -2 * log(2)
     end
 
@@ -147,7 +147,7 @@
 
         chains_pg = sample(TestModel(), AdvancedPS.PG(10), 100)
 
-        @test all(isone(p.trajectory.f.x) for p in chains_pg)
+        @test all(isone(p.trajectory.model.f.x) for p in chains_pg)
         @test mean(x.logevidence for x in chains_pg) ≈ -2 * log(2) atol = 0.01
     end
 
@@ -170,8 +170,8 @@
         pg = AdvancedPS.PG(1)
         first, second = sample(Model(), pg, 2)
 
-        first_model = first.trajectory.f
-        second_model = second.trajectory.f
+        first_model = first.trajectory.model.f
+        second_model = second.trajectory.model.f
 
         # Single Particle - must be replaying
         @test first_model.a ≈ second_model.a